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amazonml-api-006 | amazonml-api.pdf | 6 | for Ruby V3 See Also API Version 2014-12-12 20 MachineLearning API Reference CreateDataSourceFromRedshift Creates a DataSource from a database hosted on an Amazon Redshift cluster. A DataSource references data that can be used to perform either CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations. CreateDataSourceFromRedshift is an asynchronous operation. In response to CreateDataSourceFromRedshift, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource is created and ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in COMPLETED or PENDING states can be used to perform only CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations. If Amazon ML can't accept the input source, it sets the Status parameter to FAILED and includes an error message in the Message attribute of the GetDataSource operation response. The observations should be contained in the database hosted on an Amazon Redshift cluster and should be specified by a SelectSqlQuery query. Amazon ML executes an Unload command in Amazon Redshift to transfer the result set of the SelectSqlQuery query to S3StagingLocation. After the DataSource has been created, it's ready for use in evaluations and batch predictions. If you plan to use the DataSource to train an MLModel, the DataSource also requires a recipe. A recipe describes how each input variable will be used in training an MLModel. Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it be combined with another variable or will it be split apart into word combinations? The recipe provides answers to these questions. You can't change an existing datasource, but you can copy and modify the settings from an existing Amazon Redshift datasource to create a new datasource. To do so, call GetDataSource for an existing datasource and copy the values to a CreateDataSource call. Change the settings that you want to change and make sure that all required fields have the appropriate values. Request Syntax { "ComputeStatistics": boolean, "DataSourceId": "string", "DataSourceName": "string", "DataSpec": { CreateDataSourceFromRedshift API Version 2014-12-12 21 MachineLearning API Reference "DatabaseCredentials": { "Password": "string", "Username": "string" }, "DatabaseInformation": { "ClusterIdentifier": "string", "DatabaseName": "string" }, "DataRearrangement": "string", "DataSchema": "string", "DataSchemaUri": "string", "S3StagingLocation": "string", "SelectSqlQuery": "string" }, "RoleARN": "string" } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. ComputeStatistics The compute statistics for a DataSource. The statistics are generated from the observation data referenced by a DataSource. Amazon ML uses the statistics internally during MLModel training. This parameter must be set to true if the DataSource needs to be used for MLModel training. Type: Boolean Required: No DataSourceId A user-supplied ID that uniquely identifies the DataSource. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Required: Yes Request Parameters API Version 2014-12-12 22 MachineLearning DataSourceName API Reference A user-supplied name or description of the DataSource. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No DataSpec The data specification of an Amazon Redshift DataSource: • DatabaseInformation - • DatabaseName - The name of the Amazon Redshift database. • ClusterIdentifier - The unique ID for the Amazon Redshift cluster. • DatabaseCredentials - The AWS Identity and Access Management (IAM) credentials that are used to connect to the Amazon Redshift database. • SelectSqlQuery - The query that is used to retrieve the observation data for the Datasource. • S3StagingLocation - The Amazon Simple Storage Service (Amazon S3) location for staging Amazon Redshift data. The data retrieved from Amazon Redshift using the SelectSqlQuery query is stored in this location. • DataSchemaUri - The Amazon S3 location of the DataSchema. • DataSchema - A JSON string representing the schema. This is not required if DataSchemaUri is specified. • DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the DataSource. Sample - "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}" Type: RedshiftDataSpec object Required: Yes RoleARN A fully specified role Amazon Resource Name (ARN). Amazon ML assumes the role on behalf of the user to create the following: Request Parameters API Version 2014-12-12 23 MachineLearning API Reference • A security group to allow Amazon ML to execute the SelectSqlQuery query on an Amazon Redshift cluster • An Amazon S3 bucket policy to grant Amazon ML read/write permissions on the S3StagingLocation Type: String Length Constraints: Minimum length of 1. Maximum length of 110. Required: Yes Response Syntax { "DataSourceId": "string" } Response Elements If the action is successful, the service sends back an HTTP 200 response. The following data is returned in JSON format by the service. DataSourceId A user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the DataSourceID in the request. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Errors For information about the errors that are common |
amazonml-api-007 | amazonml-api.pdf | 7 | grant Amazon ML read/write permissions on the S3StagingLocation Type: String Length Constraints: Minimum length of 1. Maximum length of 110. Required: Yes Response Syntax { "DataSourceId": "string" } Response Elements If the action is successful, the service sends back an HTTP 200 response. The following data is returned in JSON format by the service. DataSourceId A user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the DataSourceID in the request. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Errors For information about the errors that are common to all actions, see Common Errors. Response Syntax API Version 2014-12-12 24 MachineLearning API Reference IdempotentParameterMismatchException A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request. HTTP Status Code: 400 InternalServerException An error on the server occurred when trying to process a request. HTTP Status Code: 500 InvalidInputException An error on the client occurred. Typically, the cause is an invalid input value. HTTP Status Code: 400 Examples The following is a sample request and response of the CreateDataSourceFromRedshift operation. This example illustrates one usage of CreateDataSourceFromRedshift. Sample Request POST / HTTP/1.1 Host: machinelearning.<region>.<domain> x-amz-Date: <Date> Authorization: AWS4-HMAC-SHA256 Credential=<Credential>, SignedHeaders=contenttype;date;host;user-agent;x-amz-date;x-amz-target;x-amzn- requestid,Signature=<Signature> User-Agent: <UserAgentString> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Connection: Keep-Alive X-Amz-Target: AmazonML_20141212.CreateDataSourceFromRedshift { "DataSourceId": "ds-exampleDatasourceId", "DataSourceName": "exampleDatasourceName", "DataSpec": Examples API Version 2014-12-12 25 API Reference MachineLearning { "DatabaseInformation": { "DatabaseName": "dev", "ClusterIdentifier": "test-cluster-1234" }, "SelectSqlQuery": "select * from table", "DatabaseCredentials": { "Username": "foo", "Password": "foo" }, "S3StagingLocation": "s3://bucketName/", "DataSchemaUri": "s3://bucketName/locationToUri/example.schema.json"}, "RoleARN": "arn:aws:iam::<awsAccountId>:role/username" } } Sample Response HTTP/1.1 200 OK x-amzn-RequestId: <RequestId> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Date: <Date> {"DataSourceId": "ds-exampleDatasourceId"} See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS Command Line Interface • AWS SDK for .NET • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin See Also API Version 2014-12-12 26 MachineLearning • AWS SDK for PHP V3 • AWS SDK for Python • AWS SDK for Ruby V3 API Reference See Also API Version 2014-12-12 27 MachineLearning API Reference CreateDataSourceFromS3 Creates a DataSource object. A DataSource references data that can be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations. CreateDataSourceFromS3 is an asynchronous operation. In response to CreateDataSourceFromS3, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource has been created and is ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in the COMPLETED or PENDING state can be used to perform only CreateMLModel, CreateEvaluation or CreateBatchPrediction operations. If Amazon ML can't accept the input source, it sets the Status parameter to FAILED and includes an error message in the Message attribute of the GetDataSource operation response. The observation data used in a DataSource should be ready to use; that is, it should have a consistent structure, and missing data values should be kept to a minimum. The observation data must reside in one or more .csv files in an Amazon Simple Storage Service (Amazon S3) location, along with a schema that describes the data items by name and type. The same schema must be used for all of the data files referenced by the DataSource. After the DataSource has been created, it's ready to use in evaluations and batch predictions. If you plan to use the DataSource to train an MLModel, the DataSource also needs a recipe. A recipe describes how each input variable will be used in training an MLModel. Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it be combined with another variable or will it be split apart into word combinations? The recipe provides answers to these questions. Request Syntax { "ComputeStatistics": boolean, "DataSourceId": "string", "DataSourceName": "string", "DataSpec": { "DataLocationS3": "string", "DataRearrangement": "string", "DataSchema": "string", "DataSchemaLocationS3": "string" CreateDataSourceFromS3 API Version 2014-12-12 28 MachineLearning } } Request Parameters API Reference For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. ComputeStatistics The compute statistics for a DataSource. The statistics are generated from the observation data referenced by a DataSource. Amazon ML uses the statistics internally during MLModel training. This parameter must be set to true if the DataSource needs to be used for MLModel training. Type: Boolean Required: No DataSourceId A user-supplied identifier that uniquely identifies the DataSource. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Required: Yes DataSourceName A user-supplied name or description of the DataSource. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: |
amazonml-api-008 | amazonml-api.pdf | 8 | the following data in JSON format. ComputeStatistics The compute statistics for a DataSource. The statistics are generated from the observation data referenced by a DataSource. Amazon ML uses the statistics internally during MLModel training. This parameter must be set to true if the DataSource needs to be used for MLModel training. Type: Boolean Required: No DataSourceId A user-supplied identifier that uniquely identifies the DataSource. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Required: Yes DataSourceName A user-supplied name or description of the DataSource. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No Request Parameters API Version 2014-12-12 29 MachineLearning DataSpec API Reference The data specification of a DataSource: • DataLocationS3 - The Amazon S3 location of the observation data. • DataSchemaLocationS3 - The Amazon S3 location of the DataSchema. • DataSchema - A JSON string representing the schema. This is not required if DataSchemaUri is specified. • DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the Datasource. Sample - "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}" Type: S3DataSpec object Required: Yes Response Syntax { "DataSourceId": "string" } Response Elements If the action is successful, the service sends back an HTTP 200 response. The following data is returned in JSON format by the service. DataSourceId A user-supplied ID that uniquely identifies the DataSource. This value should be identical to the value of the DataSourceID in the request. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Response Syntax API Version 2014-12-12 30 MachineLearning Errors API Reference For information about the errors that are common to all actions, see Common Errors. IdempotentParameterMismatchException A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request. HTTP Status Code: 400 InternalServerException An error on the server occurred when trying to process a request. HTTP Status Code: 500 InvalidInputException An error on the client occurred. Typically, the cause is an invalid input value. HTTP Status Code: 400 Examples The following is a sample request and response of the CreateDataSourceFromS3 operation. This example illustrates one usage of CreateDataSourceFromS3. Sample Request POST / HTTP/1.1 Host: machinelearning.<region>.<domain> x-amz-Date: <Date> Authorization: AWS4-HMAC-SHA256 Credential=<Credential>, SignedHeaders=contenttype;date;host;user-agent;x-amz-date;x-amz-target;x-amzn- requestid,Signature=<Signature> User-Agent: <UserAgentString> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Connection: Keep-Alive X-Amz-Target: AmazonML_20141212.CreateDataSourceFromS3 Errors API Version 2014-12-12 31 MachineLearning { "DataSourceId": "exampleDataSourceId", "DataSourceName": "exampleDataSourceName", "DataSpec": { API Reference "DataLocationS3": "s3://eml-test-EXAMPLE/data.csv", "DataSchemaLocationS3": "s3://eml-test-EXAMPLE/data.csv.schema", "DataRearrangement": "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}" } } Sample Response HTTP/1.1 200 OK x-amzn-RequestId: <RequestId> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Date: <Date> {"DataSourceId":"exampleDataSourceId"} See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS Command Line Interface • AWS SDK for .NET • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python • AWS SDK for Ruby V3 See Also API Version 2014-12-12 32 MachineLearning CreateEvaluation API Reference Creates a new Evaluation of an MLModel. An MLModel is evaluated on a set of observations associated to a DataSource. Like a DataSource for an MLModel, the DataSource for an Evaluation contains values for the Target Variable. The Evaluation compares the predicted result for each observation to the actual outcome and provides a summary so that you know how effective the MLModel functions on the test data. Evaluation generates a relevant performance metric, such as BinaryAUC, RegressionRMSE or MulticlassAvgFScore based on the corresponding MLModelType: BINARY, REGRESSION or MULTICLASS. CreateEvaluation is an asynchronous operation. In response to CreateEvaluation, Amazon Machine Learning (Amazon ML) immediately returns and sets the evaluation status to PENDING. After the Evaluation is created and ready for use, Amazon ML sets the status to COMPLETED. You can use the GetEvaluation operation to check progress of the evaluation during the creation operation. Request Syntax { "EvaluationDataSourceId": "string", "EvaluationId": "string", "EvaluationName": "string", "MLModelId": "string" } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. EvaluationDataSourceId The ID of the DataSource for the evaluation. The schema of the DataSource must match the schema used to create the MLModel. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. CreateEvaluation API Version 2014-12-12 33 MachineLearning API Reference Pattern: [a-zA-Z0-9_.-]+ Required: Yes EvaluationId A user-supplied ID that uniquely identifies the Evaluation. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Required: Yes EvaluationName A user-supplied name or description of the Evaluation. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No MLModelId The ID of the MLModel to evaluate. The schema used in creating the MLModel must match the schema |
amazonml-api-009 | amazonml-api.pdf | 9 | the DataSource must match the schema used to create the MLModel. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. CreateEvaluation API Version 2014-12-12 33 MachineLearning API Reference Pattern: [a-zA-Z0-9_.-]+ Required: Yes EvaluationId A user-supplied ID that uniquely identifies the Evaluation. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Required: Yes EvaluationName A user-supplied name or description of the Evaluation. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No MLModelId The ID of the MLModel to evaluate. The schema used in creating the MLModel must match the schema of the DataSource used in the Evaluation. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Required: Yes Response Syntax { Response Syntax API Version 2014-12-12 34 MachineLearning "EvaluationId": "string" } Response Elements API Reference If the action is successful, the service sends back an HTTP 200 response. The following data is returned in JSON format by the service. EvaluationId The user-supplied ID that uniquely identifies the Evaluation. This value should be identical to the value of the EvaluationId in the request. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Errors For information about the errors that are common to all actions, see Common Errors. IdempotentParameterMismatchException A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request. HTTP Status Code: 400 InternalServerException An error on the server occurred when trying to process a request. HTTP Status Code: 500 InvalidInputException An error on the client occurred. Typically, the cause is an invalid input value. HTTP Status Code: 400 Response Elements API Version 2014-12-12 35 MachineLearning Examples API Reference The following is a sample request and response of the CreateEvaluation operation: This example illustrates one usage of CreateEvaluation. Sample Request POST / HTTP/1.1 Host: machinelearning.<region>.<domain> x-amz-Date: <Date> Authorization: AWS4-HMAC-SHA256 Credential=<Credential>, SignedHeaders=contenttype;date;host;user-agent;x-amz-date;x-amz-target;x-amzn- requestid,Signature=<Signature> User-Agent: <UserAgentString> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Connection: Keep-Alive X-Amz-Target: AmazonML_20141212.CreateEvaluation { "EvaluationId": "CreateEvaluation-pr-2014-09-12-15-14-04-924", "EvaluationName": "EXAMPLE", "MLModelId": "EXAMPLE-pr-2014-09-12-15-14-04-924", "EvaluationDataSourceId": "EXAMPLE-ev-ds-2014-09-12-15-14-04-411", } Sample Response HTTP/1.1 200 OK x-amzn-RequestId: <RequestId> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Date: <Date> {"EvaluationId":"CreateEvaluation-pr-2014-09-12-15-14-04-924"} See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS Command Line Interface Examples API Version 2014-12-12 36 API Reference MachineLearning • AWS SDK for .NET • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python • AWS SDK for Ruby V3 See Also API Version 2014-12-12 37 MachineLearning CreateMLModel API Reference Creates a new MLModel using the DataSource and the recipe as information sources. An MLModel is nearly immutable. Users can update only the MLModelName and the ScoreThreshold in an MLModel without creating a new MLModel. CreateMLModel is an asynchronous operation. In response to CreateMLModel, Amazon Machine Learning (Amazon ML) immediately returns and sets the MLModel status to PENDING. After the MLModel has been created and ready is for use, Amazon ML sets the status to COMPLETED. You can use the GetMLModel operation to check the progress of the MLModel during the creation operation. CreateMLModel requires a DataSource with computed statistics, which can be created by setting ComputeStatistics to true in CreateDataSourceFromRDS, CreateDataSourceFromS3, or CreateDataSourceFromRedshift operations. Request Syntax { "MLModelId": "string", "MLModelName": "string", "MLModelType": "string", "Parameters": { "string" : "string" }, "Recipe": "string", "RecipeUri": "string", "TrainingDataSourceId": "string" } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. MLModelId A user-supplied ID that uniquely identifies the MLModel. CreateMLModel API Version 2014-12-12 38 MachineLearning Type: String API Reference Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Required: Yes MLModelName A user-supplied name or description of the MLModel. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No MLModelType The category of supervised learning that this MLModel will address. Choose from the following types: • Choose REGRESSION if the MLModel will be used to predict a numeric value. • Choose BINARY if the MLModel result has two possible values. • Choose MULTICLASS if the MLModel result has a limited number of values. For more information, see the Amazon Machine Learning Developer Guide. Type: String Valid Values: REGRESSION | BINARY | MULTICLASS Required: Yes Parameters A list of the training parameters in the MLModel. The list is implemented as a map of key-value pairs. The following is the current set of training parameters: • sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance. |
amazonml-api-010 | amazonml-api.pdf | 10 | numeric value. • Choose BINARY if the MLModel result has two possible values. • Choose MULTICLASS if the MLModel result has a limited number of values. For more information, see the Amazon Machine Learning Developer Guide. Type: String Valid Values: REGRESSION | BINARY | MULTICLASS Required: Yes Parameters A list of the training parameters in the MLModel. The list is implemented as a map of key-value pairs. The following is the current set of training parameters: • sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance. Request Parameters API Version 2014-12-12 39 MachineLearning API Reference The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432. • sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 100. The default value is 10. • sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none. We strongly recommend that you shuffle your data. • sgd.l1RegularizationAmount - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08. The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2 is specified. Use this parameter sparingly. • sgd.l2RegularizationAmount - The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08. The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1 is specified. Use this parameter sparingly. Type: String to string map Required: No Recipe The data recipe for creating the MLModel. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default. Type: String Length Constraints: Maximum length of 131071. Required: No Request Parameters API Version 2014-12-12 40 MachineLearning RecipeUri API Reference The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel recipe. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default. Type: String Length Constraints: Maximum length of 2048. Pattern: s3://([^/]+)(/.*)? Required: No TrainingDataSourceId The DataSource that points to the training data. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Required: Yes Response Syntax { "MLModelId": "string" } Response Elements If the action is successful, the service sends back an HTTP 200 response. The following data is returned in JSON format by the service. MLModelId A user-supplied ID that uniquely identifies the MLModel. This value should be identical to the value of the MLModelId in the request. Response Syntax API Version 2014-12-12 41 MachineLearning Type: String API Reference Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Errors For information about the errors that are common to all actions, see Common Errors. IdempotentParameterMismatchException A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request. HTTP Status Code: 400 InternalServerException An error on the server occurred when trying to process a request. HTTP Status Code: 500 InvalidInputException An error on the client occurred. Typically, the cause is an invalid input value. HTTP Status Code: 400 Examples The following is a sample request and response of the CreateMLModel operation. This example illustrates one usage of CreateMLModel. Sample Request POST / HTTP/1.1 Host: machinelearning.<region>.<domain> x-amz-Date: <Date> Authorization: AWS4-HMAC-SHA256 Credential=<Credential>, SignedHeaders=contenttype;date;host;user-agent;x-amz-date;x-amz-target;x-amzn- requestid,Signature=<Signature> Errors API Version 2014-12-12 42 MachineLearning API Reference User-Agent: <UserAgentString> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Connection: Keep-Alive X-Amz-Target: AmazonML_20141212.CreateMLModel { "MLModelId": "exampleModelId", "MLModelName": "EXAMPLE", "MLModelType": "BINARY", "TrainingDataSourceId": "17SdAv6WC6r5vACAxF7U", "RecipeUri": "s3://eml-test-EXAMPLE/data.recipe.json" } Sample Response HTTP/1.1 200 OK x-amzn-RequestId: <RequestId> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Date: <Date> {"MLModelId":"exampleModelId"} See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS Command Line Interface • AWS SDK for .NET • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python • |
amazonml-api-011 | amazonml-api.pdf | 11 | <PayloadSizeBytes> Connection: Keep-Alive X-Amz-Target: AmazonML_20141212.CreateMLModel { "MLModelId": "exampleModelId", "MLModelName": "EXAMPLE", "MLModelType": "BINARY", "TrainingDataSourceId": "17SdAv6WC6r5vACAxF7U", "RecipeUri": "s3://eml-test-EXAMPLE/data.recipe.json" } Sample Response HTTP/1.1 200 OK x-amzn-RequestId: <RequestId> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Date: <Date> {"MLModelId":"exampleModelId"} See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS Command Line Interface • AWS SDK for .NET • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python • AWS SDK for Ruby V3 See Also API Version 2014-12-12 43 MachineLearning API Reference CreateRealtimeEndpoint Creates a real-time endpoint for the MLModel. The endpoint contains the URI of the MLModel; that is, the location to send real-time prediction requests for the specified MLModel. Request Syntax { "MLModelId": "string" } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. MLModelId The ID assigned to the MLModel during creation. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Required: Yes Response Syntax { "MLModelId": "string", "RealtimeEndpointInfo": { "CreatedAt": number, "EndpointStatus": "string", "EndpointUrl": "string", "PeakRequestsPerSecond": number } } CreateRealtimeEndpoint API Version 2014-12-12 44 MachineLearning Response Elements API Reference If the action is successful, the service sends back an HTTP 200 response. The following data is returned in JSON format by the service. MLModelId A user-supplied ID that uniquely identifies the MLModel. This value should be identical to the value of the MLModelId in the request. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ RealtimeEndpointInfo The endpoint information of the MLModel Type: RealtimeEndpointInfo object Errors For information about the errors that are common to all actions, see Common Errors. InternalServerException An error on the server occurred when trying to process a request. HTTP Status Code: 500 InvalidInputException An error on the client occurred. Typically, the cause is an invalid input value. HTTP Status Code: 400 ResourceNotFoundException A specified resource cannot be located. HTTP Status Code: 400 Response Elements API Version 2014-12-12 45 MachineLearning Examples API Reference The following is a sample request and response of the CreateRealtimeEndpoint operation. This example illustrates one usage of CreateRealtimeEndpoint. Sample Request POST / HTTP/1.1 Host: machinelearning.<region>.<domain> x-amz-Date: <Date> Authorization: AWS4-HMAC-SHA256 Credential=<Credential>, SignedHeaders=contenttype;date;host;user-agent;x-amz-date;x-amz-target;x-amzn- requestid,Signature=<Signature> User-Agent: <UserAgentString> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Connection: Keep-Alive X-Amz-Target: AmazonML_20141212.CreateRealtimeEndpoint { "MLModelId": "ml-ModelExampleId", } Sample Response HTTP/1.1 200 OK x-amzn-RequestId: <RequestId> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Date: <Date> { "MLModelId": "ml-ModelExampleId", "EndpointInfo": { "CreatedAt": 1422488124.71, "EndpointUrl": "<realtime endpoint from Amazon Machine Learning for ml- ModelExampleId>", "EndpointStatus": "READY", "PeakRequestsPerSecond": 200 } } Examples API Version 2014-12-12 46 MachineLearning See Also API Reference For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS Command Line Interface • AWS SDK for .NET • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python • AWS SDK for Ruby V3 See Also API Version 2014-12-12 47 MachineLearning API Reference DeleteBatchPrediction Assigns the DELETED status to a BatchPrediction, rendering it unusable. After using the DeleteBatchPrediction operation, you can use the GetBatchPrediction operation to verify that the status of the BatchPrediction changed to DELETED. Caution: The result of the DeleteBatchPrediction operation is irreversible. Request Syntax { "BatchPredictionId": "string" } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. BatchPredictionId A user-supplied ID that uniquely identifies the BatchPrediction. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Required: Yes Response Syntax { "BatchPredictionId": "string" } DeleteBatchPrediction API Version 2014-12-12 48 MachineLearning Response Elements API Reference If the action is successful, the service sends back an HTTP 200 response. The following data is returned in JSON format by the service. BatchPredictionId A user-supplied ID that uniquely identifies the BatchPrediction. This value should be identical to the value of the BatchPredictionID in the request. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Errors For information about the errors that are common to all actions, see Common Errors. InternalServerException An error on the server occurred when trying to process a request. HTTP Status Code: 500 InvalidInputException An error on the client occurred. Typically, the cause is an invalid input value. HTTP Status Code: 400 ResourceNotFoundException A specified resource cannot be located. HTTP Status Code: 400 Response Elements API Version 2014-12-12 49 MachineLearning Examples API Reference The following is a sample request and response of the |
amazonml-api-012 | amazonml-api.pdf | 12 | BatchPredictionID in the request. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Errors For information about the errors that are common to all actions, see Common Errors. InternalServerException An error on the server occurred when trying to process a request. HTTP Status Code: 500 InvalidInputException An error on the client occurred. Typically, the cause is an invalid input value. HTTP Status Code: 400 ResourceNotFoundException A specified resource cannot be located. HTTP Status Code: 400 Response Elements API Version 2014-12-12 49 MachineLearning Examples API Reference The following is a sample request and response of the DeleteBatchPrediction operation. This example illustrates one usage of DeleteBatchPrediction. Sample Request POST / HTTP/1.1 Host: machinelearning.<region>.<domain> x-amz-Date: <Date> Authorization: AWS4-HMAC-SHA256 Credential=<Credential>, SignedHeaders=contenttype;date;host;user-agent;x-amz-date;x-amz-target;x-amzn- requestid,Signature=<Signature> User-Agent: <UserAgentString> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Connection: Keep-Alive X-Amz-Target: AmazonML_20141212.DeleteBatchPrediction {"BatchPredictionId": "exampleBatchPredictionId"} Sample Response HTTP/1.1 200 OK x-amzn-RequestId: <RequestId> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Date: <Date> {"BatchPredictionId":"exampleBatchPredictionId"} See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS Command Line Interface • AWS SDK for .NET • AWS SDK for C++ • AWS SDK for Go v2 Examples API Version 2014-12-12 50 MachineLearning • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python • AWS SDK for Ruby V3 API Reference See Also API Version 2014-12-12 51 MachineLearning DeleteDataSource API Reference Assigns the DELETED status to a DataSource, rendering it unusable. After using the DeleteDataSource operation, you can use the GetDataSource operation to verify that the status of the DataSource changed to DELETED. Caution: The results of the DeleteDataSource operation are irreversible. Request Syntax { "DataSourceId": "string" } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. DataSourceId A user-supplied ID that uniquely identifies the DataSource. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Required: Yes Response Syntax { "DataSourceId": "string" } DeleteDataSource API Version 2014-12-12 52 MachineLearning Response Elements API Reference If the action is successful, the service sends back an HTTP 200 response. The following data is returned in JSON format by the service. DataSourceId A user-supplied ID that uniquely identifies the DataSource. This value should be identical to the value of the DataSourceID in the request. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Errors For information about the errors that are common to all actions, see Common Errors. InternalServerException An error on the server occurred when trying to process a request. HTTP Status Code: 500 InvalidInputException An error on the client occurred. Typically, the cause is an invalid input value. HTTP Status Code: 400 ResourceNotFoundException A specified resource cannot be located. HTTP Status Code: 400 Response Elements API Version 2014-12-12 53 MachineLearning Examples API Reference The following is a sample request and response of the DeleteDataSource operation: This example illustrates one usage of DeleteDataSource. Sample Request POST / HTTP/1.1 Host: machinelearning.<region>.<domain> x-amz-Date: <Date> Authorization: AWS4-HMAC-SHA256 Credential=<Credential>, SignedHeaders=contenttype;date;host;user-agent;x-amz-date;x-amz-target;x-amzn- requestid,Signature=<Signature> User-Agent: <UserAgentString> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Connection: Keep-Alive X-Amz-Target: AmazonML_20141212.DeleteDataSource {"DataSourceId": "exampleDataSourceId"} Sample Response HTTP/1.1 200 OK x-amzn-RequestId: <RequestId> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Date: <Date> {"DataSourceId":"exampleDataSourceId"} See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS Command Line Interface • AWS SDK for .NET • AWS SDK for C++ • AWS SDK for Go v2 Examples API Version 2014-12-12 54 MachineLearning • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python • AWS SDK for Ruby V3 API Reference See Also API Version 2014-12-12 55 MachineLearning DeleteEvaluation API Reference Assigns the DELETED status to an Evaluation, rendering it unusable. After invoking the DeleteEvaluation operation, you can use the GetEvaluation operation to verify that the status of the Evaluation changed to DELETED. Caution: The results of the DeleteEvaluation operation are irreversible. Request Syntax { "EvaluationId": "string" } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. EvaluationId A user-supplied ID that uniquely identifies the Evaluation to delete. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Required: Yes Response Syntax { "EvaluationId": "string" } DeleteEvaluation API Version 2014-12-12 56 MachineLearning Response Elements API Reference If the action is successful, the service sends back an HTTP 200 response. The following data is returned in JSON format by the service. EvaluationId A user-supplied ID that uniquely identifies the Evaluation. This value should be identical to the value of the EvaluationId in the request. |
amazonml-api-013 | amazonml-api.pdf | 13 | Parameters. The request accepts the following data in JSON format. EvaluationId A user-supplied ID that uniquely identifies the Evaluation to delete. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Required: Yes Response Syntax { "EvaluationId": "string" } DeleteEvaluation API Version 2014-12-12 56 MachineLearning Response Elements API Reference If the action is successful, the service sends back an HTTP 200 response. The following data is returned in JSON format by the service. EvaluationId A user-supplied ID that uniquely identifies the Evaluation. This value should be identical to the value of the EvaluationId in the request. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Errors For information about the errors that are common to all actions, see Common Errors. InternalServerException An error on the server occurred when trying to process a request. HTTP Status Code: 500 InvalidInputException An error on the client occurred. Typically, the cause is an invalid input value. HTTP Status Code: 400 ResourceNotFoundException A specified resource cannot be located. HTTP Status Code: 400 Response Elements API Version 2014-12-12 57 MachineLearning Examples API Reference The following is a sample request and response of the DeleteEvaluation operation. This example illustrates one usage of DeleteEvaluation. Sample Request POST / HTTP/1.1 Host: machinelearning.<region>.<domain> x-amz-Date: <Date> Authorization: AWS4-HMAC-SHA256 Credential=<Credential>, SignedHeaders=contenttype;date;host;user-agent;x-amz-date;x-amz-target;x-amzn- requestid,Signature=<Signature> User-Agent: <UserAgentString> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Connection: Keep-Alive X-Amz-Target: AmazonML_20141212.DeleteEvaluation {"EvaluationId": "exampleEvaluationId"} Sample Response HTTP/1.1 200 OK x-amzn-RequestId: <RequestId> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Date: <Date> {"EvaluationId":"exampleEvaluationId"} See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS Command Line Interface • AWS SDK for .NET • AWS SDK for C++ • AWS SDK for Go v2 Examples API Version 2014-12-12 58 MachineLearning • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python • AWS SDK for Ruby V3 API Reference See Also API Version 2014-12-12 59 MachineLearning DeleteMLModel API Reference Assigns the DELETED status to an MLModel, rendering it unusable. After using the DeleteMLModel operation, you can use the GetMLModel operation to verify that the status of the MLModel changed to DELETED. Caution: The result of the DeleteMLModel operation is irreversible. Request Syntax { "MLModelId": "string" } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. MLModelId A user-supplied ID that uniquely identifies the MLModel. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Required: Yes Response Syntax { "MLModelId": "string" } DeleteMLModel API Version 2014-12-12 60 MachineLearning Response Elements API Reference If the action is successful, the service sends back an HTTP 200 response. The following data is returned in JSON format by the service. MLModelId A user-supplied ID that uniquely identifies the MLModel. This value should be identical to the value of the MLModelID in the request. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Errors For information about the errors that are common to all actions, see Common Errors. InternalServerException An error on the server occurred when trying to process a request. HTTP Status Code: 500 InvalidInputException An error on the client occurred. Typically, the cause is an invalid input value. HTTP Status Code: 400 ResourceNotFoundException A specified resource cannot be located. HTTP Status Code: 400 Examples The following is a sample request and response of the DeleteMLModel operation: This example illustrates one usage of DeleteMLModel. Response Elements API Version 2014-12-12 61 MachineLearning Sample Request POST / HTTP/1.1 Host: machinelearning.<region>.<domain> x-amz-Date: <Date> Authorization: AWS4-HMAC-SHA256 Credential=<Credential>, SignedHeaders=contenttype;date;host;user-agent;x-amz-date;x-amz-target;x-amzn- API Reference requestid,Signature=<Signature> User-Agent: <UserAgentString> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Connection: Keep-Alive X-Amz-Target: AmazonML_20141212.DeleteMLModel {"MLModelId": "exampleMLModelId"} Sample Response HTTP/1.1 200 OK x-amzn-RequestId: <RequestId> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Date: <Date> {"MLModelId": "exampleMLModelId"} See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS Command Line Interface • AWS SDK for .NET • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python See Also API Version 2014-12-12 62 MachineLearning • AWS SDK for Ruby V3 API Reference See Also API Version 2014-12-12 63 MachineLearning API Reference DeleteRealtimeEndpoint Deletes a real time endpoint of an MLModel. Request Syntax { "MLModelId": "string" } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. MLModelId The ID assigned to the MLModel during creation. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Required: Yes |
amazonml-api-014 | amazonml-api.pdf | 14 | • AWS SDK for PHP V3 • AWS SDK for Python See Also API Version 2014-12-12 62 MachineLearning • AWS SDK for Ruby V3 API Reference See Also API Version 2014-12-12 63 MachineLearning API Reference DeleteRealtimeEndpoint Deletes a real time endpoint of an MLModel. Request Syntax { "MLModelId": "string" } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. MLModelId The ID assigned to the MLModel during creation. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Required: Yes Response Syntax { "MLModelId": "string", "RealtimeEndpointInfo": { "CreatedAt": number, "EndpointStatus": "string", "EndpointUrl": "string", "PeakRequestsPerSecond": number } } DeleteRealtimeEndpoint API Version 2014-12-12 64 MachineLearning Response Elements API Reference If the action is successful, the service sends back an HTTP 200 response. The following data is returned in JSON format by the service. MLModelId A user-supplied ID that uniquely identifies the MLModel. This value should be identical to the value of the MLModelId in the request. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ RealtimeEndpointInfo The endpoint information of the MLModel Type: RealtimeEndpointInfo object Errors For information about the errors that are common to all actions, see Common Errors. InternalServerException An error on the server occurred when trying to process a request. HTTP Status Code: 500 InvalidInputException An error on the client occurred. Typically, the cause is an invalid input value. HTTP Status Code: 400 ResourceNotFoundException A specified resource cannot be located. Response Elements API Version 2014-12-12 65 MachineLearning HTTP Status Code: 400 Examples API Reference The following is a sample request and response of the DeleteRealtimeEndpoint operation. This example illustrates one usage of DeleteRealtimeEndpoint. Sample Request POST / HTTP/1.1 Host: machinelearning.<region>.<domain> x-amz-Date: <Date> Authorization: AWS4-HMAC-SHA256 Credential=<Credential>, SignedHeaders=contenttype;date;host;user-agent;x-amz-date;x-amz-target;x-amzn- requestid,Signature=<Signature> User-Agent: <UserAgentString> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Connection: Keep-Alive X-Amz-Target: AmazonML_20141212.DeleteRealtimeEndpoint { "MLModelId": "ml-ModelExampleId", } Sample Response HTTP/1.1 200 OK x-amzn-RequestId: <RequestId> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Date: <Date> { "MLModelId": "ml-ModelExampleId", "EndpointInfo": { "EndpointStatus": "NONE", "PeakRequestsPerSecond": 0 } } Examples API Version 2014-12-12 66 MachineLearning See Also API Reference For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS Command Line Interface • AWS SDK for .NET • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python • AWS SDK for Ruby V3 See Also API Version 2014-12-12 67 MachineLearning DeleteTags API Reference Deletes the specified tags associated with an ML object. After this operation is complete, you can't recover deleted tags. If you specify a tag that doesn't exist, Amazon ML ignores it. Request Syntax { "ResourceId": "string", "ResourceType": "string", "TagKeys": [ "string" ] } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. ResourceId The ID of the tagged ML object. For example, exampleModelId. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Required: Yes ResourceType The type of the tagged ML object. Type: String Valid Values: BatchPrediction | DataSource | Evaluation | MLModel Required: Yes DeleteTags API Version 2014-12-12 68 API Reference MachineLearning TagKeys One or more tags to delete. Type: Array of strings Array Members: Maximum number of 100 items. Length Constraints: Minimum length of 1. Maximum length of 128. Pattern: ^([\p{L}\p{Z}\p{N}_.:/=+\-@]*)$ Required: Yes Response Syntax { "ResourceId": "string", "ResourceType": "string" } Response Elements If the action is successful, the service sends back an HTTP 200 response. The following data is returned in JSON format by the service. ResourceId The ID of the ML object from which tags were deleted. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ ResourceType The type of the ML object from which tags were deleted. Type: String Response Syntax API Version 2014-12-12 69 MachineLearning API Reference Valid Values: BatchPrediction | DataSource | Evaluation | MLModel Errors For information about the errors that are common to all actions, see Common Errors. InternalServerException An error on the server occurred when trying to process a request. HTTP Status Code: 500 InvalidInputException An error on the client occurred. Typically, the cause is an invalid input value. HTTP Status Code: 400 InvalidTagException A submitted tag is invalid. HTTP Status Code: 400 ResourceNotFoundException A specified resource cannot be located. HTTP Status Code: 400 Examples The following are an example request and response for the DeleteTags operation. This example illustrates one usage of DeleteTags. Sample Request POST / HTTP/1.1 Host: machinelearning.<region>.<domain> x-amz-Date: <Date> Authorization: AWS4-HMAC-SHA256 Credential=<Credential>, SignedHeaders=contenttype;date;host;user-agent;x-amz-date;x-amz-target;x-amzn- requestid,Signature=<Signature> Errors API Version 2014-12-12 70 MachineLearning API Reference User-Agent: <UserAgentString> Content-Type: application/x-amz-json-1.1 Content-Length: |
amazonml-api-015 | amazonml-api.pdf | 15 | the server occurred when trying to process a request. HTTP Status Code: 500 InvalidInputException An error on the client occurred. Typically, the cause is an invalid input value. HTTP Status Code: 400 InvalidTagException A submitted tag is invalid. HTTP Status Code: 400 ResourceNotFoundException A specified resource cannot be located. HTTP Status Code: 400 Examples The following are an example request and response for the DeleteTags operation. This example illustrates one usage of DeleteTags. Sample Request POST / HTTP/1.1 Host: machinelearning.<region>.<domain> x-amz-Date: <Date> Authorization: AWS4-HMAC-SHA256 Credential=<Credential>, SignedHeaders=contenttype;date;host;user-agent;x-amz-date;x-amz-target;x-amzn- requestid,Signature=<Signature> Errors API Version 2014-12-12 70 MachineLearning API Reference User-Agent: <UserAgentString> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Connection: Keep-Alive X-Amz-Target: AmazonML_20141212.DeleteTags { "ResourceId": "exampleModelId", "ResourceType": "MLModel", "Tags": [ "exampleKey" ] } Sample Response HTTP/1.1 200 OK x-amzn-RequestId: <RequestId> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Date: <Date> { "ResourceId": "exampleModelId", "ResourceType": "MLModel" } See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS Command Line Interface • AWS SDK for .NET • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python See Also API Version 2014-12-12 71 MachineLearning • AWS SDK for Ruby V3 API Reference See Also API Version 2014-12-12 72 MachineLearning API Reference DescribeBatchPredictions Returns a list of BatchPrediction operations that match the search criteria in the request. Request Syntax { "EQ": "string", "FilterVariable": "string", "GE": "string", "GT": "string", "LE": "string", "Limit": number, "LT": "string", "NE": "string", "NextToken": "string", "Prefix": "string", "SortOrder": "string" } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. EQ The equal to operator. The BatchPrediction results will have FilterVariable values that exactly match the value specified with EQ. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No FilterVariable Use one of the following variables to filter a list of BatchPrediction: DescribeBatchPredictions API Version 2014-12-12 73 MachineLearning API Reference • CreatedAt - Sets the search criteria to the BatchPrediction creation date. • Status - Sets the search criteria to the BatchPrediction status. • Name - Sets the search criteria to the contents of the BatchPrediction Name. • IAMUser - Sets the search criteria to the user account that invoked the BatchPrediction creation. • MLModelId - Sets the search criteria to the MLModel used in the BatchPrediction. • DataSourceId - Sets the search criteria to the DataSource used in the BatchPrediction. • DataURI - Sets the search criteria to the data file(s) used in the BatchPrediction. The URL can identify either a file or an Amazon Simple Storage Solution (Amazon S3) bucket or directory. Type: String Valid Values: CreatedAt | LastUpdatedAt | Status | Name | IAMUser | MLModelId | DataSourceId | DataURI Required: No GE The greater than or equal to operator. The BatchPrediction results will have FilterVariable values that are greater than or equal to the value specified with GE. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No GT The greater than operator. The BatchPrediction results will have FilterVariable values that are greater than the value specified with GT. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Request Parameters API Version 2014-12-12 74 MachineLearning Required: No LE API Reference The less than or equal to operator. The BatchPrediction results will have FilterVariable values that are less than or equal to the value specified with LE. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No Limit The number of pages of information to include in the result. The range of acceptable values is 1 through 100. The default value is 100. Type: Integer Valid Range: Minimum value of 1. Maximum value of 100. Required: No LT The less than operator. The BatchPrediction results will have FilterVariable values that are less than the value specified with LT. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No NE The not equal to operator. The BatchPrediction results will have FilterVariable values not equal to the value specified with NE. Type: String Length Constraints: Maximum length of 1024. Request Parameters API Version 2014-12-12 75 API Reference MachineLearning Pattern: .*\S.*|^$ Required: No NextToken An ID of the page in the paginated results. Type: String Required: No Prefix A string that is found at the beginning of a variable, such as Name or Id. For example, a Batch Prediction operation could have the Name 2014-09-09- HolidayGiftMailer. To search for this BatchPrediction, select Name for the FilterVariable and any of the following strings for the Prefix: • 2014-09 • 2014-09-09 • 2014-09-09-Holiday Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No SortOrder A two-value parameter that |
amazonml-api-016 | amazonml-api.pdf | 16 | of 1024. Request Parameters API Version 2014-12-12 75 API Reference MachineLearning Pattern: .*\S.*|^$ Required: No NextToken An ID of the page in the paginated results. Type: String Required: No Prefix A string that is found at the beginning of a variable, such as Name or Id. For example, a Batch Prediction operation could have the Name 2014-09-09- HolidayGiftMailer. To search for this BatchPrediction, select Name for the FilterVariable and any of the following strings for the Prefix: • 2014-09 • 2014-09-09 • 2014-09-09-Holiday Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No SortOrder A two-value parameter that determines the sequence of the resulting list of MLModels. • asc - Arranges the list in ascending order (A-Z, 0-9). • dsc - Arranges the list in descending order (Z-A, 9-0). Results are sorted by FilterVariable. Type: String Valid Values: asc | dsc Required: No Request Parameters API Version 2014-12-12 76 API Reference MachineLearning Response Syntax { "NextToken": "string", "Results": [ { "BatchPredictionDataSourceId": "string", "BatchPredictionId": "string", "ComputeTime": number, "CreatedAt": number, "CreatedByIamUser": "string", "FinishedAt": number, "InputDataLocationS3": "string", "InvalidRecordCount": number, "LastUpdatedAt": number, "Message": "string", "MLModelId": "string", "Name": "string", "OutputUri": "string", "StartedAt": number, "Status": "string", "TotalRecordCount": number } ] } Response Elements If the action is successful, the service sends back an HTTP 200 response. The following data is returned in JSON format by the service. NextToken The ID of the next page in the paginated results that indicates at least one more page follows. Type: String Results A list of BatchPrediction objects that meet the search criteria. Type: Array of BatchPrediction objects Response Syntax API Version 2014-12-12 77 MachineLearning Errors API Reference For information about the errors that are common to all actions, see Common Errors. InternalServerException An error on the server occurred when trying to process a request. HTTP Status Code: 500 InvalidInputException An error on the client occurred. Typically, the cause is an invalid input value. HTTP Status Code: 400 Examples The following is a sample request and response of the DescribeBatchPredictions operation. This example illustrates one usage of DescribeBatchPredictions. Sample Request POST / HTTP/1.1 Host: machinelearning.<region>.<domain> x-amz-Date: <Date> Authorization: AWS4-HMAC-SHA256 Credential=<Credential>, SignedHeaders=contenttype;date;host;user-agent;x-amz-date;x-amz-target;x-amzn- requestid,Signature=<Signature> User-Agent: <UserAgentString> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Connection: Keep-Alive X-Amz-Target: AmazonML_20141212.DescribeBatchPredictions { "FilterVariable": "Name", "Prefix": "bp-", "SortOrder": "asc", "Limit": 1 } Errors API Version 2014-12-12 78 API Reference MachineLearning Sample Response HTTP/1.1 200 OK x-amzn-RequestId: <RequestId> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Date: <Date> { "Results": [ { "BatchPredictionDataSourceId": "ds-exampleDataSourceId", "BatchPredictionId": "bp-exampleBatchPredictionId", "CreatedAt": 1422057670.697, "CreatedByIamUser": "arn:aws:iam::<awsAccountId>:user/username", "InputDataLocationS3": "s3://bucket/locationToInput/example-data.testing.csv", "LastUpdatedAt": 1422057811.431, "MLModelId": "pr-exampleModelId", "Name": "bp-exampleBatchPredictionName", "OutputUri": "s3://bucket/locationToLogs/", "Status": "COMPLETED", "ComputeTime":"185200", "FinishedAt":"1422057711.192", "StartedAt":"14220557678.324", "TotalRecordCount":"21154", "InvalidRecordCount":"0" } ] } See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS Command Line Interface • AWS SDK for .NET • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java V2 • AWS SDK for JavaScript V3 See Also API Version 2014-12-12 79 MachineLearning • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python • AWS SDK for Ruby V3 API Reference See Also API Version 2014-12-12 80 MachineLearning API Reference DescribeDataSources Returns a list of DataSource that match the search criteria in the request. Request Syntax { "EQ": "string", "FilterVariable": "string", "GE": "string", "GT": "string", "LE": "string", "Limit": number, "LT": "string", "NE": "string", "NextToken": "string", "Prefix": "string", "SortOrder": "string" } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. EQ The equal to operator. The DataSource results will have FilterVariable values that exactly match the value specified with EQ. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No FilterVariable Use one of the following variables to filter a list of DataSource: • CreatedAt - Sets the search criteria to DataSource creation dates. DescribeDataSources API Version 2014-12-12 81 MachineLearning API Reference • Status - Sets the search criteria to DataSource statuses. • Name - Sets the search criteria to the contents of DataSource Name. • DataUri - Sets the search criteria to the URI of data files used to create the DataSource. The URI can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory. • IAMUser - Sets the search criteria to the user account that invoked the DataSource creation. Type: String Valid Values: CreatedAt | LastUpdatedAt | Status | Name | DataLocationS3 | IAMUser Required: No GE The greater than or equal to operator. The DataSource results will have FilterVariable values that are greater than or equal to the value specified with GE. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No GT The greater than operator. The DataSource results will have FilterVariable values that are greater than the value specified with GT. |
amazonml-api-017 | amazonml-api.pdf | 17 | Storage Service (Amazon S3) bucket or directory. • IAMUser - Sets the search criteria to the user account that invoked the DataSource creation. Type: String Valid Values: CreatedAt | LastUpdatedAt | Status | Name | DataLocationS3 | IAMUser Required: No GE The greater than or equal to operator. The DataSource results will have FilterVariable values that are greater than or equal to the value specified with GE. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No GT The greater than operator. The DataSource results will have FilterVariable values that are greater than the value specified with GT. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No LE The less than or equal to operator. The DataSource results will have FilterVariable values that are less than or equal to the value specified with LE. Request Parameters API Version 2014-12-12 82 API Reference MachineLearning Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No Limit The maximum number of DataSource to include in the result. Type: Integer Valid Range: Minimum value of 1. Maximum value of 100. Required: No LT The less than operator. The DataSource results will have FilterVariable values that are less than the value specified with LT. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No NE The not equal to operator. The DataSource results will have FilterVariable values not equal to the value specified with NE. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No NextToken The ID of the page in the paginated results. Request Parameters API Version 2014-12-12 83 MachineLearning Type: String Required: No Prefix API Reference A string that is found at the beginning of a variable, such as Name or Id. For example, a DataSource could have the Name 2014-09-09-HolidayGiftMailer. To search for this DataSource, select Name for the FilterVariable and any of the following strings for the Prefix: • 2014-09 • 2014-09-09 • 2014-09-09-Holiday Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No SortOrder A two-value parameter that determines the sequence of the resulting list of DataSource. • asc - Arranges the list in ascending order (A-Z, 0-9). • dsc - Arranges the list in descending order (Z-A, 9-0). Results are sorted by FilterVariable. Type: String Valid Values: asc | dsc Required: No Response Syntax { "NextToken": "string", "Results": [ Response Syntax API Version 2014-12-12 84 MachineLearning API Reference { "ComputeStatistics": boolean, "ComputeTime": number, "CreatedAt": number, "CreatedByIamUser": "string", "DataLocationS3": "string", "DataRearrangement": "string", "DataSizeInBytes": number, "DataSourceId": "string", "FinishedAt": number, "LastUpdatedAt": number, "Message": "string", "Name": "string", "NumberOfFiles": number, "RDSMetadata": { "Database": { "DatabaseName": "string", "InstanceIdentifier": "string" }, "DatabaseUserName": "string", "DataPipelineId": "string", "ResourceRole": "string", "SelectSqlQuery": "string", "ServiceRole": "string" }, "RedshiftMetadata": { "DatabaseUserName": "string", "RedshiftDatabase": { "ClusterIdentifier": "string", "DatabaseName": "string" }, "SelectSqlQuery": "string" }, "RoleARN": "string", "StartedAt": number, "Status": "string" } ] } Response Elements If the action is successful, the service sends back an HTTP 200 response. Response Elements API Version 2014-12-12 85 MachineLearning API Reference The following data is returned in JSON format by the service. NextToken An ID of the next page in the paginated results that indicates at least one more page follows. Type: String Results A list of DataSource that meet the search criteria. Type: Array of DataSource objects Errors For information about the errors that are common to all actions, see Common Errors. InternalServerException An error on the server occurred when trying to process a request. HTTP Status Code: 500 InvalidInputException An error on the client occurred. Typically, the cause is an invalid input value. HTTP Status Code: 400 Examples The following is a sample request and response of the DescribeDataSources operation. This example illustrates one usage of DescribeDataSources. Sample Request POST / HTTP/1.1 Host: machinelearning.<region>.<domain> x-amz-Date: <Date> Errors API Version 2014-12-12 86 MachineLearning API Reference Authorization: AWS4-HMAC-SHA256 Credential=<Credential>, SignedHeaders=contenttype;date;host;user-agent;x-amz-date;x-amz-target;x-amzn- requestid,Signature=<Signature> User-Agent: <UserAgentString> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Connection: Keep-Alive X-Amz-Target: AmazonML_20141212.DescribeDataSources { "FilterVariable": "Name", "Prefix": "bp-", "SortOrder": "asc", "Limit": 1 } Sample Response HTTP/1.1 200 OK x-amzn-RequestId: <RequestId> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Date: <Date> { "NextToken": "{\"DataSourceId\":\"ds-exampleDataSource2\"}", "Results": [ { "ComputeStatistics": true, "CreatedAt": 1428008286.077, "CreatedByIamUser": "arn:aws:iam::<awsAccountId>:user/username", "DataLocationS3": "s3://bucket/locationToInput/example-data.testing.csv", "DataSourceId": "ds-exampleDatasourceId", "LastUpdatedAt": 1428018286.654, "Name": "exampleDatasource", "Status": "COMPLETED", "ComputeTime":"185200", "FinishedAt":"1428018286.654", "StartedAt":"1428008287.324" }, { "ComputeStatistics": true, "CreatedAt": 1428008286.077, "CreatedByIamUser": "arn:aws:iam::<awsAccountId>:user/username", "DataLocationS3": "s3://bucket/locationToInput/example-data.testing.csv", Examples API Version 2014-12-12 87 MachineLearning API Reference "DataSourceId": "ds-exampleDatasourceId", "LastUpdatedAt": 1428018286.654, "Name": "exampleDatasource", "Status": "PENDING" } ] } See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS Command Line Interface • AWS SDK for .NET • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python • AWS SDK for Ruby V3 See Also |
amazonml-api-018 | amazonml-api.pdf | 18 | 1428008286.077, "CreatedByIamUser": "arn:aws:iam::<awsAccountId>:user/username", "DataLocationS3": "s3://bucket/locationToInput/example-data.testing.csv", Examples API Version 2014-12-12 87 MachineLearning API Reference "DataSourceId": "ds-exampleDatasourceId", "LastUpdatedAt": 1428018286.654, "Name": "exampleDatasource", "Status": "PENDING" } ] } See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS Command Line Interface • AWS SDK for .NET • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python • AWS SDK for Ruby V3 See Also API Version 2014-12-12 88 MachineLearning API Reference DescribeEvaluations Returns a list of DescribeEvaluations that match the search criteria in the request. Request Syntax { "EQ": "string", "FilterVariable": "string", "GE": "string", "GT": "string", "LE": "string", "Limit": number, "LT": "string", "NE": "string", "NextToken": "string", "Prefix": "string", "SortOrder": "string" } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. EQ The equal to operator. The Evaluation results will have FilterVariable values that exactly match the value specified with EQ. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No FilterVariable Use one of the following variable to filter a list of Evaluation objects: DescribeEvaluations API Version 2014-12-12 89 MachineLearning API Reference • CreatedAt - Sets the search criteria to the Evaluation creation date. • Status - Sets the search criteria to the Evaluation status. • Name - Sets the search criteria to the contents of Evaluation Name. • IAMUser - Sets the search criteria to the user account that invoked an Evaluation. • MLModelId - Sets the search criteria to the MLModel that was evaluated. • DataSourceId - Sets the search criteria to the DataSource used in Evaluation. • DataUri - Sets the search criteria to the data file(s) used in Evaluation. The URL can identify either a file or an Amazon Simple Storage Solution (Amazon S3) bucket or directory. Type: String Valid Values: CreatedAt | LastUpdatedAt | Status | Name | IAMUser | MLModelId | DataSourceId | DataURI Required: No GE The greater than or equal to operator. The Evaluation results will have FilterVariable values that are greater than or equal to the value specified with GE. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No GT The greater than operator. The Evaluation results will have FilterVariable values that are greater than the value specified with GT. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No Request Parameters API Version 2014-12-12 90 MachineLearning LE API Reference The less than or equal to operator. The Evaluation results will have FilterVariable values that are less than or equal to the value specified with LE. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No Limit The maximum number of Evaluation to include in the result. Type: Integer Valid Range: Minimum value of 1. Maximum value of 100. Required: No LT The less than operator. The Evaluation results will have FilterVariable values that are less than the value specified with LT. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No NE The not equal to operator. The Evaluation results will have FilterVariable values not equal to the value specified with NE. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Request Parameters API Version 2014-12-12 91 MachineLearning Required: No NextToken The ID of the page in the paginated results. Type: String Required: No Prefix API Reference A string that is found at the beginning of a variable, such as Name or Id. For example, an Evaluation could have the Name 2014-09-09-HolidayGiftMailer. To search for this Evaluation, select Name for the FilterVariable and any of the following strings for the Prefix: • 2014-09 • 2014-09-09 • 2014-09-09-Holiday Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No SortOrder A two-value parameter that determines the sequence of the resulting list of Evaluation. • asc - Arranges the list in ascending order (A-Z, 0-9). • dsc - Arranges the list in descending order (Z-A, 9-0). Results are sorted by FilterVariable. Type: String Valid Values: asc | dsc Required: No Request Parameters API Version 2014-12-12 92 API Reference MachineLearning Response Syntax { "NextToken": "string", "Results": [ { "ComputeTime": number, "CreatedAt": number, "CreatedByIamUser": "string", "EvaluationDataSourceId": "string", "EvaluationId": "string", "FinishedAt": number, "InputDataLocationS3": "string", "LastUpdatedAt": number, "Message": "string", "MLModelId": "string", "Name": "string", "PerformanceMetrics": { "Properties": { "string" : "string" } }, "StartedAt": number, "Status": "string" } ] } Response Elements If the action is successful, the service sends back an HTTP 200 response. The following data is returned in JSON format by the service. NextToken The ID of the next page in the paginated results that indicates at least one |
amazonml-api-019 | amazonml-api.pdf | 19 | dsc Required: No Request Parameters API Version 2014-12-12 92 API Reference MachineLearning Response Syntax { "NextToken": "string", "Results": [ { "ComputeTime": number, "CreatedAt": number, "CreatedByIamUser": "string", "EvaluationDataSourceId": "string", "EvaluationId": "string", "FinishedAt": number, "InputDataLocationS3": "string", "LastUpdatedAt": number, "Message": "string", "MLModelId": "string", "Name": "string", "PerformanceMetrics": { "Properties": { "string" : "string" } }, "StartedAt": number, "Status": "string" } ] } Response Elements If the action is successful, the service sends back an HTTP 200 response. The following data is returned in JSON format by the service. NextToken The ID of the next page in the paginated results that indicates at least one more page follows. Type: String Results A list of Evaluation that meet the search criteria. Response Syntax API Version 2014-12-12 93 MachineLearning API Reference Type: Array of Evaluation objects Errors For information about the errors that are common to all actions, see Common Errors. InternalServerException An error on the server occurred when trying to process a request. HTTP Status Code: 500 InvalidInputException An error on the client occurred. Typically, the cause is an invalid input value. HTTP Status Code: 400 Examples The following is a sample request and response of the DescribeEvaluations operation. This example illustrates one usage of DescribeEvaluations. Sample Request POST / HTTP/1.1 Host: machinelearning.<region>.<domain> x-amz-Date: <Date> Authorization: AWS4-HMAC-SHA256 Credential=<Credential>, SignedHeaders=contenttype;date;host;user-agent;x-amz-date;x-amz-target;x-amzn- requestid,Signature=<Signature> User-Agent: <UserAgentString> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Connection: Keep-Alive X-Amz-Target: AmazonML_20141212.DescribeEvaluations { "FilterVariable": "Name", "Prefix": "ev-", Errors API Version 2014-12-12 94 API Reference MachineLearning "SortOrder": "asc", "Limit": 1 } Sample Response HTTP/1.1 200 OK x-amzn-RequestId: <RequestId> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Date: <Date> { "NextToken": "{\"EvaluationId\":\"ev-exampleId2\"}", "Results": [ { "CreatedAt": 1420745248.785, "CreatedByIamUser": "arn:aws:iam::<awsAccountId>:user/username", "EvaluationDataSourceId": "ds-exampleDataSourceId", "EvaluationId": "ev-exampleId1", "InputDataLocationS3": "s3://bucket/locationToInput/example-data.testing.csv", "LastUpdatedAt": 1420745524.506, "MLModelId": "pr-exampleModelId", "Name": "ev-1", "PerformanceMetrics": { "Properties": {"BinaryAUC": "0.9228827246570067"} }, "Status": "COMPLETED", "ComputeTime":"185200", "FinishedAt":"1420745524.506", "StartedAt":"1420745249.324" } ] } See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS Command Line Interface • AWS SDK for .NET See Also API Version 2014-12-12 95 API Reference MachineLearning • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python • AWS SDK for Ruby V3 See Also API Version 2014-12-12 96 MachineLearning DescribeMLModels API Reference Returns a list of MLModel that match the search criteria in the request. Request Syntax { "EQ": "string", "FilterVariable": "string", "GE": "string", "GT": "string", "LE": "string", "Limit": number, "LT": "string", "NE": "string", "NextToken": "string", "Prefix": "string", "SortOrder": "string" } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. EQ The equal to operator. The MLModel results will have FilterVariable values that exactly match the value specified with EQ. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No FilterVariable Use one of the following variables to filter a list of MLModel: DescribeMLModels API Version 2014-12-12 97 MachineLearning API Reference • CreatedAt - Sets the search criteria to MLModel creation date. • Status - Sets the search criteria to MLModel status. • Name - Sets the search criteria to the contents of MLModel Name. • IAMUser - Sets the search criteria to the user account that invoked the MLModel creation. • TrainingDataSourceId - Sets the search criteria to the DataSource used to train one or more MLModel. • RealtimeEndpointStatus - Sets the search criteria to the MLModel real-time endpoint status. • MLModelType - Sets the search criteria to MLModel type: binary, regression, or multi-class. • Algorithm - Sets the search criteria to the algorithm that the MLModel uses. • TrainingDataURI - Sets the search criteria to the data file(s) used in training a MLModel. The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory. Type: String Valid Values: CreatedAt | LastUpdatedAt | Status | Name | IAMUser | TrainingDataSourceId | RealtimeEndpointStatus | MLModelType | Algorithm | TrainingDataURI Required: No GE The greater than or equal to operator. The MLModel results will have FilterVariable values that are greater than or equal to the value specified with GE. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No GT The greater than operator. The MLModel results will have FilterVariable values that are greater than the value specified with GT. Request Parameters API Version 2014-12-12 98 MachineLearning Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No LE API Reference The less than or equal to operator. The MLModel results will have FilterVariable values that are less than or equal to the value specified with LE. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No Limit The number of pages of information to include in the result. |
amazonml-api-020 | amazonml-api.pdf | 20 | length of 1024. Pattern: .*\S.*|^$ Required: No GT The greater than operator. The MLModel results will have FilterVariable values that are greater than the value specified with GT. Request Parameters API Version 2014-12-12 98 MachineLearning Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No LE API Reference The less than or equal to operator. The MLModel results will have FilterVariable values that are less than or equal to the value specified with LE. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No Limit The number of pages of information to include in the result. The range of acceptable values is 1 through 100. The default value is 100. Type: Integer Valid Range: Minimum value of 1. Maximum value of 100. Required: No LT The less than operator. The MLModel results will have FilterVariable values that are less than the value specified with LT. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No Request Parameters API Version 2014-12-12 99 MachineLearning NE API Reference The not equal to operator. The MLModel results will have FilterVariable values not equal to the value specified with NE. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No NextToken The ID of the page in the paginated results. Type: String Required: No Prefix A string that is found at the beginning of a variable, such as Name or Id. For example, an MLModel could have the Name 2014-09-09-HolidayGiftMailer. To search for this MLModel, select Name for the FilterVariable and any of the following strings for the Prefix: • 2014-09 • 2014-09-09 • 2014-09-09-Holiday Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No SortOrder A two-value parameter that determines the sequence of the resulting list of MLModel. • asc - Arranges the list in ascending order (A-Z, 0-9). Request Parameters API Version 2014-12-12 100 MachineLearning API Reference • dsc - Arranges the list in descending order (Z-A, 9-0). Results are sorted by FilterVariable. Type: String Valid Values: asc | dsc Required: No Response Syntax { "NextToken": "string", "Results": [ { "Algorithm": "string", "ComputeTime": number, "CreatedAt": number, "CreatedByIamUser": "string", "EndpointInfo": { "CreatedAt": number, "EndpointStatus": "string", "EndpointUrl": "string", "PeakRequestsPerSecond": number }, "FinishedAt": number, "InputDataLocationS3": "string", "LastUpdatedAt": number, "Message": "string", "MLModelId": "string", "MLModelType": "string", "Name": "string", "ScoreThreshold": number, "ScoreThresholdLastUpdatedAt": number, "SizeInBytes": number, "StartedAt": number, "Status": "string", "TrainingDataSourceId": "string", "TrainingParameters": { "string" : "string" } } Response Syntax API Version 2014-12-12 101 MachineLearning ] } Response Elements API Reference If the action is successful, the service sends back an HTTP 200 response. The following data is returned in JSON format by the service. NextToken The ID of the next page in the paginated results that indicates at least one more page follows. Type: String Results A list of MLModel that meet the search criteria. Type: Array of MLModel objects Errors For information about the errors that are common to all actions, see Common Errors. InternalServerException An error on the server occurred when trying to process a request. HTTP Status Code: 500 InvalidInputException An error on the client occurred. Typically, the cause is an invalid input value. HTTP Status Code: 400 Examples The following is a sample request and response of the DescribeMLModels operation: This example illustrates one usage of DescribeMLModels. Response Elements API Version 2014-12-12 102 MachineLearning Sample Request POST / HTTP/1.1 Host: machinelearning.<region>.<domain> x-amz-Date: <Date> Authorization: AWS4-HMAC-SHA256 Credential=<Credential>, SignedHeaders=contenttype;date;host;user-agent;x-amz-date;x-amz-target;x-amzn- API Reference requestid,Signature=<Signature> User-Agent: <UserAgentString> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Connection: Keep-Alive X-Amz-Target: AmazonML_20141212.DescribeMLModels { "FilterVariable": "Name", "Prefix": "ml-", "SortOrder": "asc", "Limit": 1 } Sample Response HTTP/1.1 200 OK x-amzn-RequestId: <RequestId> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Date: <Date> { "NextToken": "\"PredictorId\":\"Spr-ml-model-testing\"}", "Results": [ { "CreatedAt": 1422475435.595, "CreatedByIamUser": "arn:aws:iam::<awsAccountId>:user/username", "InputDataLocationS3": "s3://bucket/locationToInput/example-data.testing.csv", "LastUpdatedAt": 1422475709.691, "MLModelId": "ml-model-testing", "MLModelType": "MULTICLASS", "EndpointInfo": { "CreatedAt": 1424378682.266, "EndpointStatus": "READY", "EndpointUrl": "<realtime endpoint from Amazon Machine Learning for ml-model- testing>", "PeakRequestsPerSecond": 200} Examples API Version 2014-12-12 103 MachineLearning API Reference "Name": "ml-model-name", "Algorithm": "sgd", "SizeInBytes": 352720, "Status": "COMPLETED", "ComputeTime":"185200", "FinishedAt":"1422475709.691", "StartedAt":"1422475438.324", "TrainingDataSourceId": "exampleDataSourceId", "TrainingParameters": { "algorithm": "sgd", "sgd.l1RegularizationAmount": "0.0", "sgd.l2RegularizationAmount": "1E-6", "sgd.maxMLModelSizeInBytes": "33554432", "sgd.maxPasses": "10" } } ] } See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS Command Line Interface • AWS SDK for .NET • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python • AWS SDK for Ruby V3 See Also API Version 2014-12-12 104 MachineLearning DescribeTags Describes one or more of the tags for your Amazon ML object. API Reference Request Syntax { "ResourceId": "string", "ResourceType": "string" } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON |
amazonml-api-021 | amazonml-api.pdf | 21 | .NET • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python • AWS SDK for Ruby V3 See Also API Version 2014-12-12 104 MachineLearning DescribeTags Describes one or more of the tags for your Amazon ML object. API Reference Request Syntax { "ResourceId": "string", "ResourceType": "string" } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. ResourceId The ID of the ML object. For example, exampleModelId. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Required: Yes ResourceType The type of the ML object. Type: String Valid Values: BatchPrediction | DataSource | Evaluation | MLModel Required: Yes Response Syntax { DescribeTags API Version 2014-12-12 105 MachineLearning API Reference "ResourceId": "string", "ResourceType": "string", "Tags": [ { "Key": "string", "Value": "string" } ] } Response Elements If the action is successful, the service sends back an HTTP 200 response. The following data is returned in JSON format by the service. ResourceId The ID of the tagged ML object. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ ResourceType The type of the tagged ML object. Type: String Valid Values: BatchPrediction | DataSource | Evaluation | MLModel Tags A list of tags associated with the ML object. Type: Array of Tag objects Array Members: Maximum number of 100 items. Errors For information about the errors that are common to all actions, see Common Errors. Response Elements API Version 2014-12-12 106 MachineLearning InternalServerException API Reference An error on the server occurred when trying to process a request. HTTP Status Code: 500 InvalidInputException An error on the client occurred. Typically, the cause is an invalid input value. HTTP Status Code: 400 ResourceNotFoundException A specified resource cannot be located. HTTP Status Code: 400 Examples The following are an example request and response for the DescribeTags operation. This example illustrates one usage of DescribeTags. Sample Request POST / HTTP/1.1 Host: machinelearning.<region>.<domain> x-amz-Date: <Date> Authorization: AWS4-HMAC-SHA256 Credential=<Credential>, SignedHeaders=contenttype;date;host;user-agent;x-amz-date;x-amz-target;x-amzn- requestid,Signature=<Signature> User-Agent: <UserAgentString> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Connection: Keep-Alive X-Amz-Target: AmazonML_20141212.DescribeTags { "ResourceId": "exampleModelId", "ResourceType": "MLModel" } Examples API Version 2014-12-12 107 API Reference MachineLearning Sample Response HTTP/1.1 200 OK x-amzn-RequestId: <RequestId> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Date: <Date> { "ResourceId": "exampleModelId", "ResourceType": "MLModel", "Tags": { "Key":"exampleKey", "Value":"exampleKeyValue" } } See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS Command Line Interface • AWS SDK for .NET • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python • AWS SDK for Ruby V3 See Also API Version 2014-12-12 108 MachineLearning GetBatchPrediction API Reference Returns a BatchPrediction that includes detailed metadata, status, and data file information for a Batch Prediction request. Request Syntax { "BatchPredictionId": "string" } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. BatchPredictionId An ID assigned to the BatchPrediction at creation. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Required: Yes Response Syntax { "BatchPredictionDataSourceId": "string", "BatchPredictionId": "string", "ComputeTime": number, "CreatedAt": number, "CreatedByIamUser": "string", "FinishedAt": number, "InputDataLocationS3": "string", "InvalidRecordCount": number, GetBatchPrediction API Version 2014-12-12 109 MachineLearning API Reference "LastUpdatedAt": number, "LogUri": "string", "Message": "string", "MLModelId": "string", "Name": "string", "OutputUri": "string", "StartedAt": number, "Status": "string", "TotalRecordCount": number } Response Elements If the action is successful, the service sends back an HTTP 200 response. The following data is returned in JSON format by the service. BatchPredictionDataSourceId The ID of the DataSource that was used to create the BatchPrediction. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ BatchPredictionId An ID assigned to the BatchPrediction at creation. This value should be identical to the value of the BatchPredictionID in the request. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ ComputeTime The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the BatchPrediction, normalized and scaled on computation resources. ComputeTime is only available if the BatchPrediction is in the COMPLETED state. Response Elements API Version 2014-12-12 110 MachineLearning Type: Long CreatedAt API Reference The time when the BatchPrediction was created. The time is expressed in epoch time. Type: Timestamp CreatedByIamUser The AWS user account that invoked the BatchPrediction. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account. Type: String Pattern: |
amazonml-api-022 | amazonml-api.pdf | 22 | 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ ComputeTime The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the BatchPrediction, normalized and scaled on computation resources. ComputeTime is only available if the BatchPrediction is in the COMPLETED state. Response Elements API Version 2014-12-12 110 MachineLearning Type: Long CreatedAt API Reference The time when the BatchPrediction was created. The time is expressed in epoch time. Type: Timestamp CreatedByIamUser The AWS user account that invoked the BatchPrediction. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account. Type: String Pattern: arn:aws:iam::[0-9]+:((user/.+)|(root)) FinishedAt The epoch time when Amazon Machine Learning marked the BatchPrediction as COMPLETED or FAILED. FinishedAt is only available when the BatchPrediction is in the COMPLETED or FAILED state. Type: Timestamp InputDataLocationS3 The location of the data file or directory in Amazon Simple Storage Service (Amazon S3). Type: String Length Constraints: Maximum length of 2048. Pattern: s3://([^/]+)(/.*)? InvalidRecordCount The number of invalid records that Amazon Machine Learning saw while processing the BatchPrediction. Type: Long LastUpdatedAt The time of the most recent edit to BatchPrediction. The time is expressed in epoch time. Response Elements API Version 2014-12-12 111 MachineLearning Type: Timestamp LogUri API Reference A link to the file that contains logs of the CreateBatchPrediction operation. Type: String Message A description of the most recent details about processing the batch prediction request. Type: String Length Constraints: Maximum length of 10240. MLModelId The ID of the MLModel that generated predictions for the BatchPrediction request. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Name A user-supplied name or description of the BatchPrediction. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ OutputUri The location of an Amazon S3 bucket or directory to receive the operation results. Type: String Length Constraints: Maximum length of 2048. Pattern: s3://([^/]+)(/.*)? Response Elements API Version 2014-12-12 112 MachineLearning StartedAt API Reference The epoch time when Amazon Machine Learning marked the BatchPrediction as INPROGRESS. StartedAt isn't available if the BatchPrediction is in the PENDING state. Type: Timestamp Status The status of the BatchPrediction, which can be one of the following values: • PENDING - Amazon Machine Learning (Amazon ML) submitted a request to generate batch predictions. • INPROGRESS - The batch predictions are in progress. • FAILED - The request to perform a batch prediction did not run to completion. It is not usable. • COMPLETED - The batch prediction process completed successfully. • DELETED - The BatchPrediction is marked as deleted. It is not usable. Type: String Valid Values: PENDING | INPROGRESS | FAILED | COMPLETED | DELETED TotalRecordCount The number of total records that Amazon Machine Learning saw while processing the BatchPrediction. Type: Long Errors For information about the errors that are common to all actions, see Common Errors. InternalServerException An error on the server occurred when trying to process a request. HTTP Status Code: 500 InvalidInputException An error on the client occurred. Typically, the cause is an invalid input value. Errors API Version 2014-12-12 113 MachineLearning HTTP Status Code: 400 ResourceNotFoundException A specified resource cannot be located. HTTP Status Code: 400 Examples API Reference The following is a sample request and response of the GetBatchPrediction operation. This example illustrates one usage of GetBatchPrediction. Sample Request POST / HTTP/1.1 Host: machinelearning.<region>.<domain> x-amz-Date: <Date> Authorization: AWS4-HMAC-SHA256 Credential=<Credential>, SignedHeaders=contenttype;date;host;user-agent;x-amz-date;x-amz-target;x-amzn- requestid,Signature=<Signature> User-Agent: <UserAgentString> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Connection: Keep-Alive X-Amz-Target: AmazonML_20141212.GetBatchPrediction {"BatchPredictionId": "EXAMPLE-bp-2014-09-12-15-14-04-156"} Sample Response HTTP/1.1 200 OK x-amzn-RequestId: <RequestId> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Date: <Date> { "BatchPredictionDataSourceId":"EXAMPLE-tr-ds-2014-09-12-15-14-04-989", "BatchPredictionId":"EXAMPLE-bp-2014-09-12-15-14-04-156", "CreatedAt":1410560632.327, "CreatedByIamUser":"arn:aws:iam::<awsAccountId>:user/user", Examples API Version 2014-12-12 114 MachineLearning API Reference "InputDataLocationS3": "s3://eml-test-EXAMPLE/example.csv", "LastUpdatedAt":1410560632.327, "LogUri": "https://s3bucket/locationToLogs/logname.tar.gz", "Name":"EXAMPLE", "OutputUri":"s3://eml-test-EXAMPLE/test-outputs/EXAMPLE-bp-2014-09-12-15-14-04-156/ results", "MLModelId":"EXAMPLE-pr-2014-09-12-15-14-04-924", "Status":"COMPLETED", "ComputeTime":"185200", "FinishedAt":1410560632.327, "StartedAt":1410560632.327, "TotalRecordCount":"21154", "InvalidRecordCount":"0" } See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS Command Line Interface • AWS SDK for .NET • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python • AWS SDK for Ruby V3 See Also API Version 2014-12-12 115 MachineLearning GetDataSource API Reference Returns a DataSource that includes metadata and data file information, as well as the current status of the DataSource. GetDataSource provides results in normal or verbose format. The verbose format adds the schema description and the list of files pointed to by the DataSource to the normal format. Request Syntax { "DataSourceId": "string", "Verbose": boolean } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. DataSourceId The ID assigned to the DataSource at creation. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: |
amazonml-api-023 | amazonml-api.pdf | 23 | that includes metadata and data file information, as well as the current status of the DataSource. GetDataSource provides results in normal or verbose format. The verbose format adds the schema description and the list of files pointed to by the DataSource to the normal format. Request Syntax { "DataSourceId": "string", "Verbose": boolean } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. DataSourceId The ID assigned to the DataSource at creation. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Required: Yes Verbose Specifies whether the GetDataSource operation should return DataSourceSchema. If true, DataSourceSchema is returned. If false, DataSourceSchema is not returned. GetDataSource API Version 2014-12-12 116 API Reference MachineLearning Type: Boolean Required: No Response Syntax { "ComputeStatistics": boolean, "ComputeTime": number, "CreatedAt": number, "CreatedByIamUser": "string", "DataLocationS3": "string", "DataRearrangement": "string", "DataSizeInBytes": number, "DataSourceId": "string", "DataSourceSchema": "string", "FinishedAt": number, "LastUpdatedAt": number, "LogUri": "string", "Message": "string", "Name": "string", "NumberOfFiles": number, "RDSMetadata": { "Database": { "DatabaseName": "string", "InstanceIdentifier": "string" }, "DatabaseUserName": "string", "DataPipelineId": "string", "ResourceRole": "string", "SelectSqlQuery": "string", "ServiceRole": "string" }, "RedshiftMetadata": { "DatabaseUserName": "string", "RedshiftDatabase": { "ClusterIdentifier": "string", "DatabaseName": "string" }, "SelectSqlQuery": "string" }, "RoleARN": "string", "StartedAt": number, Response Syntax API Version 2014-12-12 117 MachineLearning "Status": "string" } Response Elements API Reference If the action is successful, the service sends back an HTTP 200 response. The following data is returned in JSON format by the service. ComputeStatistics The parameter is true if statistics need to be generated from the observation data. Type: Boolean ComputeTime The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the DataSource, normalized and scaled on computation resources. ComputeTime is only available if the DataSource is in the COMPLETED state and the ComputeStatistics is set to true. Type: Long CreatedAt The time that the DataSource was created. The time is expressed in epoch time. Type: Timestamp CreatedByIamUser The AWS user account from which the DataSource was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account. Type: String Pattern: arn:aws:iam::[0-9]+:((user/.+)|(root)) DataLocationS3 The location of the data file or directory in Amazon Simple Storage Service (Amazon S3). Type: String Length Constraints: Maximum length of 2048. Response Elements API Version 2014-12-12 118 MachineLearning API Reference Pattern: s3://([^/]+)(/.*)? DataRearrangement A JSON string that represents the splitting and rearrangement requirement used when this DataSource was created. Type: String DataSizeInBytes The total size of observations in the data files. Type: Long DataSourceId The ID assigned to the DataSource at creation. This value should be identical to the value of the DataSourceId in the request. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ DataSourceSchema The schema used by all of the data files of this DataSource. Note: This parameter is provided as part of the verbose format. Type: String Length Constraints: Maximum length of 131071. FinishedAt The epoch time when Amazon Machine Learning marked the DataSource as COMPLETED or FAILED. FinishedAt is only available when the DataSource is in the COMPLETED or FAILED state. Type: Timestamp LastUpdatedAt The time of the most recent edit to the DataSource. The time is expressed in epoch time. Response Elements API Version 2014-12-12 119 MachineLearning Type: Timestamp LogUri API Reference A link to the file containing logs of CreateDataSourceFrom* operations. Type: String Message The user-supplied description of the most recent details about creating the DataSource. Type: String Length Constraints: Maximum length of 10240. Name A user-supplied name or description of the DataSource. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ NumberOfFiles The number of data files referenced by the DataSource. Type: Long RDSMetadata The datasource details that are specific to Amazon RDS. Type: RDSMetadata object RedshiftMetadata Describes the DataSource details specific to Amazon Redshift. Type: RedshiftMetadata object RoleARN The Amazon Resource Name (ARN) of an AWS IAM Role, such as the following: arn:aws:iam::account:role/rolename. Response Elements API Version 2014-12-12 120 MachineLearning Type: String API Reference Length Constraints: Minimum length of 1. Maximum length of 110. StartedAt The epoch time when Amazon Machine Learning marked the DataSource as INPROGRESS. StartedAt isn't available if the DataSource is in the PENDING state. Type: Timestamp Status The current status of the DataSource. This element can have one of the following values: • PENDING - Amazon ML submitted a request to create a DataSource. • INPROGRESS - The creation process is underway. • FAILED - The request to create a DataSource did not run to completion. It is not usable. • COMPLETED - The creation process completed successfully. • DELETED - The DataSource is marked as deleted. It is not usable. Type: String Valid Values: PENDING | INPROGRESS | FAILED | COMPLETED | DELETED Errors For information about the errors that are common |
amazonml-api-024 | amazonml-api.pdf | 24 | state. Type: Timestamp Status The current status of the DataSource. This element can have one of the following values: • PENDING - Amazon ML submitted a request to create a DataSource. • INPROGRESS - The creation process is underway. • FAILED - The request to create a DataSource did not run to completion. It is not usable. • COMPLETED - The creation process completed successfully. • DELETED - The DataSource is marked as deleted. It is not usable. Type: String Valid Values: PENDING | INPROGRESS | FAILED | COMPLETED | DELETED Errors For information about the errors that are common to all actions, see Common Errors. InternalServerException An error on the server occurred when trying to process a request. HTTP Status Code: 500 InvalidInputException An error on the client occurred. Typically, the cause is an invalid input value. HTTP Status Code: 400 ResourceNotFoundException A specified resource cannot be located. Errors API Version 2014-12-12 121 MachineLearning HTTP Status Code: 400 Examples API Reference The following is a sample request and response of the GetDataSource operation. This example illustrates one usage of GetDataSource. Sample Request POST / HTTP/1.1 Host: machinelearning.<region>.<domain> x-amz-Date: <Date> Authorization: AWS4-HMAC-SHA256 Credential=<Credential>, SignedHeaders=contenttype;date;host;user-agent;x-amz-date;x-amz-target;x-amzn- requestid,Signature=<Signature> User-Agent: <UserAgentString> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Connection: Keep-Alive X-Amz-Target: AmazonML_20141212.GetDataSource {"DataSourceId": "17SdAv6WC6r5vACAxF7U", "Verbose": true} Sample Response HTTP/1.1 200 OK x-amzn-RequestId: <RequestId> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Date: <Date> { { "CreatedAt":141045168.275, "CreatedByIamUser":"arn:aws:iam::<awsAccountId>:user/testuser", "DataLocationS3":"s3://eml-test-EXAMPLE /data.csv", "DataRearrangement": "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}", "DataSizeInBytes":0,"DataSourceId":"17SdAv6WC6r5vACAxF7U", "DataSourceSchema":" { \"version\":\"1.0\", \"recordAnnotationFieldName\":null, \"recordWeightFieldName\":\"weight\", Examples API Version 2014-12-12 122 MachineLearning API Reference \"targetFieldName\":\"label\", \"dataFormat\":\"CSV\", \"dataFileContainsHeader\":false, \"attributes\": [ {\"attributeName\":\"obsId\",\"attributeType\":\"NUMERIC\"}, {\"attributeName\":\"label\",\"attributeType\":\"BINARY\"}, {\"attributeName\":\"weight\",\"attributeType\":\"NUMERIC\"}, {\"attributeName\":\"x\",\"attributeType\":\"TEXT\"} ], \"excludedAttributeNames\":[] }", "DataStatisticsStatus":"COMPLETED", "LastUpdatedAt":141045168.275, "LogUri": "https://s3bucket/locationToLogs/logname.tar.gz", "Name":"EXAMPLE", "Status":"COMPLETED", "ComputeTime":"185200", "FinishedAt":141045168.275, "StartedAt":141045168.275 } } See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS Command Line Interface • AWS SDK for .NET • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python • AWS SDK for Ruby V3 See Also API Version 2014-12-12 123 MachineLearning API Reference See Also API Version 2014-12-12 124 MachineLearning GetEvaluation API Reference Returns an Evaluation that includes metadata as well as the current status of the Evaluation. Request Syntax { "EvaluationId": "string" } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. EvaluationId The ID of the Evaluation to retrieve. The evaluation of each MLModel is recorded and cataloged. The ID provides the means to access the information. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Required: Yes Response Syntax { "ComputeTime": number, "CreatedAt": number, "CreatedByIamUser": "string", "EvaluationDataSourceId": "string", "EvaluationId": "string", "FinishedAt": number, "InputDataLocationS3": "string", "LastUpdatedAt": number, GetEvaluation API Version 2014-12-12 125 MachineLearning API Reference "LogUri": "string", "Message": "string", "MLModelId": "string", "Name": "string", "PerformanceMetrics": { "Properties": { "string" : "string" } }, "StartedAt": number, "Status": "string" } Response Elements If the action is successful, the service sends back an HTTP 200 response. The following data is returned in JSON format by the service. ComputeTime The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the Evaluation, normalized and scaled on computation resources. ComputeTime is only available if the Evaluation is in the COMPLETED state. Type: Long CreatedAt The time that the Evaluation was created. The time is expressed in epoch time. Type: Timestamp CreatedByIamUser The AWS user account that invoked the evaluation. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account. Type: String Pattern: arn:aws:iam::[0-9]+:((user/.+)|(root)) EvaluationDataSourceId The DataSource used for this evaluation. Response Elements API Version 2014-12-12 126 MachineLearning Type: String API Reference Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ EvaluationId The evaluation ID which is same as the EvaluationId in the request. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ FinishedAt The epoch time when Amazon Machine Learning marked the Evaluation as COMPLETED or FAILED. FinishedAt is only available when the Evaluation is in the COMPLETED or FAILED state. Type: Timestamp InputDataLocationS3 The location of the data file or directory in Amazon Simple Storage Service (Amazon S3). Type: String Length Constraints: Maximum length of 2048. Pattern: s3://([^/]+)(/.*)? LastUpdatedAt The time of the most recent edit to the Evaluation. The time is expressed in epoch time. Type: Timestamp LogUri A link to the file that contains logs of the CreateEvaluation operation. Type: String Response Elements API Version 2014-12-12 127 MachineLearning Message API Reference A description of the most recent details about evaluating the MLModel. Type: String Length Constraints: Maximum length of 10240. MLModelId The ID of the MLModel that was the focus of the evaluation. Type: |
amazonml-api-025 | amazonml-api.pdf | 25 | the data file or directory in Amazon Simple Storage Service (Amazon S3). Type: String Length Constraints: Maximum length of 2048. Pattern: s3://([^/]+)(/.*)? LastUpdatedAt The time of the most recent edit to the Evaluation. The time is expressed in epoch time. Type: Timestamp LogUri A link to the file that contains logs of the CreateEvaluation operation. Type: String Response Elements API Version 2014-12-12 127 MachineLearning Message API Reference A description of the most recent details about evaluating the MLModel. Type: String Length Constraints: Maximum length of 10240. MLModelId The ID of the MLModel that was the focus of the evaluation. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Name A user-supplied name or description of the Evaluation. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ PerformanceMetrics Measurements of how well the MLModel performed using observations referenced by the DataSource. One of the following metric is returned based on the type of the MLModel: • BinaryAUC: A binary MLModel uses the Area Under the Curve (AUC) technique to measure performance. • RegressionRMSE: A regression MLModel uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable. • MulticlassAvgFScore: A multiclass MLModel uses the F1 score technique to measure performance. For more information about performance metrics, please see the Amazon Machine Learning Developer Guide. Response Elements API Version 2014-12-12 128 MachineLearning API Reference Type: PerformanceMetrics object StartedAt The epoch time when Amazon Machine Learning marked the Evaluation as INPROGRESS. StartedAt isn't available if the Evaluation is in the PENDING state. Type: Timestamp Status The status of the evaluation. This element can have one of the following values: • PENDING - Amazon Machine Language (Amazon ML) submitted a request to evaluate an MLModel. • INPROGRESS - The evaluation is underway. • FAILED - The request to evaluate an MLModel did not run to completion. It is not usable. • COMPLETED - The evaluation process completed successfully. • DELETED - The Evaluation is marked as deleted. It is not usable. Type: String Valid Values: PENDING | INPROGRESS | FAILED | COMPLETED | DELETED Errors For information about the errors that are common to all actions, see Common Errors. InternalServerException An error on the server occurred when trying to process a request. HTTP Status Code: 500 InvalidInputException An error on the client occurred. Typically, the cause is an invalid input value. HTTP Status Code: 400 ResourceNotFoundException A specified resource cannot be located. Errors API Version 2014-12-12 129 MachineLearning HTTP Status Code: 400 Examples API Reference The following is a sample request and response of the GetEvaluation operation. This example illustrates one usage of GetEvaluation. Sample Request POST / HTTP/1.1 Host: machinelearning.<region>.<domain> x-amz-Date: <Date> Authorization: AWS4-HMAC-SHA256 Credential=<Credential>, SignedHeaders=contenttype;date;host;user-agent;x-amz-date;x-amz-target;x-amzn- requestid,Signature=<Signature> User-Agent: <UserAgentString> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Connection: Keep-Alive X-Amz-Target: AmazonML_20141212.GetEvaluation {"EvaluationId": "ev-2014-09-12-15-14-04-924"} Sample Response HTTP/1.1 200 OK x-amzn-RequestId: <RequestId> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Date: <Date> { "CreatedAt":1410560805.669, "CreatedByIamUser":"arn:aws:iam::<awsAccountId>:user/user", "EvaluationDataSourceId":"EXAMPLE-ev-ds-2014-09-12-15-14-04-411", "EvaluationId":"ev-2014-09-12-15-14-04-924", "InputDataLocationS3": "s3://eml-test-EXAMPLE/example.csv", "LastUpdatedAt":1410560805.669, "LogUri": "https://s3bucket/locationToLogs/logname.tar.gz", "Name":"EXAMPLE", "PerformanceMetrics":{"Properties":{}}, "MLModelId":"EXAMPLE-pr-2014-09-12-15-14-04-924", "Status":"COMPLETED", Examples API Version 2014-12-12 130 MachineLearning API Reference "ComputeTime":"185200", "FinishedAt":1410560805.669, "StartedAt":1410560805.669 } See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS Command Line Interface • AWS SDK for .NET • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python • AWS SDK for Ruby V3 See Also API Version 2014-12-12 131 MachineLearning GetMLModel API Reference Returns an MLModel that includes detailed metadata, data source information, and the current status of the MLModel. GetMLModel provides results in normal or verbose format. Request Syntax { "MLModelId": "string", "Verbose": boolean } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. MLModelId The ID assigned to the MLModel at creation. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Required: Yes Verbose Specifies whether the GetMLModel operation should return Recipe. If true, Recipe is returned. If false, Recipe is not returned. Type: Boolean GetMLModel API Version 2014-12-12 132 API Reference MachineLearning Required: No Response Syntax { "ComputeTime": number, "CreatedAt": number, "CreatedByIamUser": "string", "EndpointInfo": { "CreatedAt": number, "EndpointStatus": "string", "EndpointUrl": "string", "PeakRequestsPerSecond": number }, "FinishedAt": number, "InputDataLocationS3": "string", "LastUpdatedAt": number, "LogUri": "string", "Message": "string", "MLModelId": "string", "MLModelType": "string", "Name": "string", "Recipe": "string", "Schema": "string", "ScoreThreshold": number, "ScoreThresholdLastUpdatedAt": number, "SizeInBytes": number, "StartedAt": number, "Status": "string", "TrainingDataSourceId": "string", "TrainingParameters": { "string" : "string" } } Response Elements If the action is successful, the service sends back an HTTP 200 response. The following data is returned in JSON format by the service. Response Syntax |
amazonml-api-026 | amazonml-api.pdf | 26 | returned. Type: Boolean GetMLModel API Version 2014-12-12 132 API Reference MachineLearning Required: No Response Syntax { "ComputeTime": number, "CreatedAt": number, "CreatedByIamUser": "string", "EndpointInfo": { "CreatedAt": number, "EndpointStatus": "string", "EndpointUrl": "string", "PeakRequestsPerSecond": number }, "FinishedAt": number, "InputDataLocationS3": "string", "LastUpdatedAt": number, "LogUri": "string", "Message": "string", "MLModelId": "string", "MLModelType": "string", "Name": "string", "Recipe": "string", "Schema": "string", "ScoreThreshold": number, "ScoreThresholdLastUpdatedAt": number, "SizeInBytes": number, "StartedAt": number, "Status": "string", "TrainingDataSourceId": "string", "TrainingParameters": { "string" : "string" } } Response Elements If the action is successful, the service sends back an HTTP 200 response. The following data is returned in JSON format by the service. Response Syntax API Version 2014-12-12 133 MachineLearning ComputeTime API Reference The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the MLModel, normalized and scaled on computation resources. ComputeTime is only available if the MLModel is in the COMPLETED state. Type: Long CreatedAt The time that the MLModel was created. The time is expressed in epoch time. Type: Timestamp CreatedByIamUser The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account. Type: String Pattern: arn:aws:iam::[0-9]+:((user/.+)|(root)) EndpointInfo The current endpoint of the MLModel Type: RealtimeEndpointInfo object FinishedAt The epoch time when Amazon Machine Learning marked the MLModel as COMPLETED or FAILED. FinishedAt is only available when the MLModel is in the COMPLETED or FAILED state. Type: Timestamp InputDataLocationS3 The location of the data file or directory in Amazon Simple Storage Service (Amazon S3). Type: String Length Constraints: Maximum length of 2048. Pattern: s3://([^/]+)(/.*)? Response Elements API Version 2014-12-12 134 MachineLearning LastUpdatedAt API Reference The time of the most recent edit to the MLModel. The time is expressed in epoch time. Type: Timestamp LogUri A link to the file that contains logs of the CreateMLModel operation. Type: String Message A description of the most recent details about accessing the MLModel. Type: String Length Constraints: Maximum length of 10240. MLModelId The MLModel ID, which is same as the MLModelId in the request. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ MLModelType Identifies the MLModel category. The following are the available types: • REGRESSION -- Produces a numeric result. For example, "What price should a house be listed at?" • BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?" • MULTICLASS -- Produces one of several possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?" Type: String Valid Values: REGRESSION | BINARY | MULTICLASS Name A user-supplied name or description of the MLModel. Response Elements API Version 2014-12-12 135 MachineLearning Type: String Length Constraints: Maximum length of 1024. Recipe API Reference The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training. Note: This parameter is provided as part of the verbose format. Type: String Length Constraints: Maximum length of 131071. Schema The schema used by all of the data files referenced by the DataSource. Note: This parameter is provided as part of the verbose format. Type: String Length Constraints: Maximum length of 131071. ScoreThreshold The scoring threshold is used in binary classification MLModel models. It marks the boundary between a positive prediction and a negative prediction. Output values greater than or equal to the threshold receive a positive result from the MLModel, such as true. Output values less than the threshold receive a negative response from the MLModel, such as false. Type: Float ScoreThresholdLastUpdatedAt The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time. Type: Timestamp SizeInBytes Long integer type that is a 64-bit signed number. Type: Long Response Elements API Version 2014-12-12 136 MachineLearning StartedAt API Reference The epoch time when Amazon Machine Learning marked the MLModel as INPROGRESS. StartedAt isn't available if the MLModel is in the PENDING state. Type: Timestamp Status The current status of the MLModel. This element can have one of the following values: • PENDING - Amazon Machine Learning (Amazon ML) submitted a request to describe a MLModel. • INPROGRESS - The request is processing. • FAILED - The request did not run to completion. The ML model isn't usable. • COMPLETED - The request completed successfully. • DELETED - The MLModel is marked as deleted. It isn't usable. Type: String Valid Values: PENDING | INPROGRESS | FAILED | COMPLETED | DELETED TrainingDataSourceId The ID of the training DataSource. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ TrainingParameters A list of the training parameters in the MLModel. The list is implemented as a map of key-value pairs. The following is the current set of training parameters: • sgd.maxMLModelSizeInBytes - |
amazonml-api-027 | amazonml-api.pdf | 27 | • FAILED - The request did not run to completion. The ML model isn't usable. • COMPLETED - The request completed successfully. • DELETED - The MLModel is marked as deleted. It isn't usable. Type: String Valid Values: PENDING | INPROGRESS | FAILED | COMPLETED | DELETED TrainingDataSourceId The ID of the training DataSource. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ TrainingParameters A list of the training parameters in the MLModel. The list is implemented as a map of key-value pairs. The following is the current set of training parameters: • sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance. The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432. Response Elements API Version 2014-12-12 137 MachineLearning API Reference • sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 100. The default value is 10. • sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none. We strongly recommend that you shuffle your data. • sgd.l1RegularizationAmount - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08. The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2 is specified. Use this parameter sparingly. • sgd.l2RegularizationAmount - The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08. The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1 is specified. Use this parameter sparingly. Type: String to string map Errors For information about the errors that are common to all actions, see Common Errors. InternalServerException An error on the server occurred when trying to process a request. HTTP Status Code: 500 InvalidInputException An error on the client occurred. Typically, the cause is an invalid input value. HTTP Status Code: 400 Errors API Version 2014-12-12 138 MachineLearning ResourceNotFoundException A specified resource cannot be located. HTTP Status Code: 400 Examples API Reference The following is a sample request and response of the GetMLModel operation. This example illustrates one usage of GetMLModel. Sample Request POST / HTTP/1.1 Host: machinelearning.<region>.<domain> x-amz-Date: <Date> Authorization: AWS4-HMAC-SHA256 Credential=<Credential>, SignedHeaders=contenttype;date;host;user-agent;x-amz-date;x-amz-target;x-amzn- requestid,Signature=<Signature> User-Agent: <UserAgentString> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Connection: Keep-Alive X-Amz-Target: AmazonML_20141212.GetMLModel {"MLModelId": "EXAMPLE-pr-2014-09-12-15-14-04-924", "Verbose": true} Sample Response HTTP/1.1 200 OK x-amzn-RequestId: <RequestId> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Date: <Date> { "CreatedAt":1410560408.264, "CreatedByIamUser":"arn:aws:iam::<awsAccountId>:user/user", "HasCalibration":false, "LastUpdatedAt":1410560416.338, "LogUri": "https://s3bucket/locationToLogs/logname.tar.gz", "Name":"Name-ml-model", "Algorithm": "sgd", "MLModelId":"ml-model", Examples API Version 2014-12-12 139 MachineLearning API Reference "EndpointInfo": { "CreatedAt": 1424378682.266, "EndpointStatus": "READY", "EndpointUrl": "<realtime endpoint from Amazon Machine Learning for ml-model>", "PeakRequestsPerSecond": 200} "MLModelType":"BINARY", "Recipe":"{ \"groups\": {}, \n\"assignments\": {}, \n\"dependencies\": {}, \n\"outputs\": [\n \"'x'\"\n] }\n", "Schema": "{ \"version\":\"1.0\", \"rowId\":null, \"rowWeight\":null, \"targetAttributeName\":\"y\", \"dataFormat\":\"CSV\", \"dataFileContainsHeader\":false, \"attributes\":[{\"attributeName\":\"age\", \"attributeType\":\"NUMERIC\"}, {\"attributeName\":\"job\", \"attributeType\":\"CATEGORICAL\"}, {\"attributeName\":\"contact\", \"attributeType\":\"CATEGORICAL\"}, {\"attributeName\":\"month\", \"attributeType\":\"CATEGORICAL\"}, {\"attributeName\":\"day_of_week\", \"attributeType\":\"CATEGORICAL\"}, {\"attributeName\":\"duration\", \"attributeType\":\"NUMERIC\"}, {\"attributeName\":\"poutcome\", \"attributeType\":\"CATEGORICAL\"}, {\"attributeName\":\"nr_employed\", \"attributeType\":\"NUMERIC\"}, {\"attributeName\":\"y\", \"attributeType\":\"BINARY\"}], \"excludedAttributeNames\":[]}" "SizeInBytes": 400374, "Status":"COMPLETED", "ComputeTime":"185200", "FinishedAt":1410560416.338, "StartedAt":1410560409.264, "TrainingDataSourceId":"EXAMPLE-tr-ds-2014-09-12-15-14-04-989", Examples API Version 2014-12-12 140 API Reference MachineLearning "TrainingParameters":{ "algorithm":"sgd", "sgd.l1":"0.0", "sgd.l2":"0.0", "sgd.likelihood":"logreg", "sgd.passes":"1" } } See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS Command Line Interface • AWS SDK for .NET • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python • AWS SDK for Ruby V3 See Also API Version 2014-12-12 141 MachineLearning Predict API Reference Generates a prediction for the observation using the specified ML Model. Note: Not all response parameters will be populated. Whether a response parameter is populated depends on the type of model requested. Request Syntax { "MLModelId": "string", "PredictEndpoint": "string", "Record": { "string" : "string" } } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. MLModelId A unique identifier of the MLModel. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Required: Yes PredictEndpoint The predicted endpoint for the input. Type: String Required: Yes Predict API |
amazonml-api-028 | amazonml-api.pdf | 28 | the specified ML Model. Note: Not all response parameters will be populated. Whether a response parameter is populated depends on the type of model requested. Request Syntax { "MLModelId": "string", "PredictEndpoint": "string", "Record": { "string" : "string" } } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. MLModelId A unique identifier of the MLModel. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Required: Yes PredictEndpoint The predicted endpoint for the input. Type: String Required: Yes Predict API Version 2014-12-12 142 MachineLearning Record API Reference A map of variable name-value pairs that represent an observation. Type: String to string map Required: Yes Response Syntax { "Prediction": { "details": { "string" : "string" }, "predictedLabel": "string", "predictedScores": { "string" : number }, "predictedValue": number } } Response Elements If the action is successful, the service sends back an HTTP 200 response. The following data is returned in JSON format by the service. Prediction The output from a Predict operation: • Details - Contains the following attributes: DetailsAttributes.PREDICTIVE_MODEL_TYPE - REGRESSION | BINARY | MULTICLASS DetailsAttributes.ALGORITHM - SGD • PredictedLabel - Present for either a BINARY or MULTICLASS MLModel request. • PredictedScores - Contains the raw classification score corresponding to each label. • PredictedValue - Present for a REGRESSION MLModel request. Type: Prediction object Response Syntax API Version 2014-12-12 143 MachineLearning Errors API Reference For information about the errors that are common to all actions, see Common Errors. InternalServerException An error on the server occurred when trying to process a request. HTTP Status Code: 500 InvalidInputException An error on the client occurred. Typically, the cause is an invalid input value. HTTP Status Code: 400 LimitExceededException The subscriber exceeded the maximum number of operations. This exception can occur when listing objects such as DataSource. HTTP Status Code: 400 PredictorNotMountedException The exception is thrown when a predict request is made to an unmounted MLModel. HTTP Status Code: 400 ResourceNotFoundException A specified resource cannot be located. HTTP Status Code: 400 Examples The following is a sample request and response of the Predict operation. This example illustrates one usage of Predict. Sample Request POST / HTTP/1.1 Errors API Version 2014-12-12 144 MachineLearning API Reference Host: <hostname from the GetMLModel response EndpointUrl object> x-amz-Date: <Date> Authorization: AWS4-HMAC-SHA256 Credential=<Credential>, SignedHeaders=contenttype;date;host;user-agent;x-amz-date;x-amz-target;x-amzn- requestid,Signature=<Signature> User-Agent: <UserAgentString> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Connection: Keep-Alive X-Amz-Target: AmazonML_20141212.Predict {"MLModelId" : "exampleMLModelId", "Record" : { "ExampleData" : "exampleValue" }, "PredictEndpoint" : "<realtime endpoint from Amazon Machine Learning for exampleMLModelId>" } Sample Response HTTP/1.1 200 OK x-amzn-RequestId: <RequestId> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Date: <Date> {"PredictedLabel" : "0" "PredictedScores" : { "0" : "0.446588516" }, "Details" : { "PredictiveModelType" : "BINARY", "Algorithm" : "SGD" } } See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS Command Line Interface • AWS SDK for .NET See Also API Version 2014-12-12 145 API Reference MachineLearning • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python • AWS SDK for Ruby V3 See Also API Version 2014-12-12 146 MachineLearning API Reference UpdateBatchPrediction Updates the BatchPredictionName of a BatchPrediction. You can use the GetBatchPrediction operation to view the contents of the updated data element. Request Syntax { "BatchPredictionId": "string", "BatchPredictionName": "string" } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. BatchPredictionId The ID assigned to the BatchPrediction during creation. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Required: Yes BatchPredictionName A new user-supplied name or description of the BatchPrediction. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: Yes UpdateBatchPrediction API Version 2014-12-12 147 MachineLearning Response Syntax { "BatchPredictionId": "string" } Response Elements API Reference If the action is successful, the service sends back an HTTP 200 response. The following data is returned in JSON format by the service. BatchPredictionId The ID assigned to the BatchPrediction during creation. This value should be identical to the value of the BatchPredictionId in the request. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Errors For information about the errors that are common to all actions, see Common Errors. InternalServerException An error on the server occurred when trying to process a request. HTTP Status Code: 500 InvalidInputException An error on the client occurred. Typically, the cause is an invalid input value. HTTP Status Code: 400 ResourceNotFoundException A specified resource cannot be located. Response Syntax API Version 2014-12-12 148 MachineLearning HTTP Status Code: 400 Examples |
amazonml-api-029 | amazonml-api.pdf | 29 | during creation. This value should be identical to the value of the BatchPredictionId in the request. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Errors For information about the errors that are common to all actions, see Common Errors. InternalServerException An error on the server occurred when trying to process a request. HTTP Status Code: 500 InvalidInputException An error on the client occurred. Typically, the cause is an invalid input value. HTTP Status Code: 400 ResourceNotFoundException A specified resource cannot be located. Response Syntax API Version 2014-12-12 148 MachineLearning HTTP Status Code: 400 Examples API Reference The following is a sample request and response of the UpdateBatchPrediction operation. This example illustrates one usage of UpdateBatchPrediction. Sample Request POST / HTTP/1.1 Host: machinelearning.<region>.<domain> x-amz-Date: <Date> Authorization: AWS4-HMAC-SHA256 Credential=<Credential>, SignedHeaders=contenttype;date;host;user-agent;x-amz-date;x-amz-target;x-amzn- requestid,Signature=<Signature> User-Agent: <UserAgentString> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Connection: Keep-Alive X-Amz-Target: AmazonML_20141212.UpdateBatchPrediction { "BatchPredictionId": "bp-exampleBatchPredictionId", "BatchPredictionName": "bp-exampleBatchPredictionName" } Sample Response HTTP/1.1 200 OK x-amzn-RequestId: <RequestId> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Date: <Date> {"BatchPredictionId": "bp-exampleBatchPredictionId"} See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: Examples API Version 2014-12-12 149 API Reference MachineLearning • AWS Command Line Interface • AWS SDK for .NET • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python • AWS SDK for Ruby V3 See Also API Version 2014-12-12 150 MachineLearning UpdateDataSource Updates the DataSourceName of a DataSource. API Reference You can use the GetDataSource operation to view the contents of the updated data element. Request Syntax { "DataSourceId": "string", "DataSourceName": "string" } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. DataSourceId The ID assigned to the DataSource during creation. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Required: Yes DataSourceName A new user-supplied name or description of the DataSource that will replace the current description. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: Yes UpdateDataSource API Version 2014-12-12 151 MachineLearning Response Syntax { "DataSourceId": "string" } Response Elements API Reference If the action is successful, the service sends back an HTTP 200 response. The following data is returned in JSON format by the service. DataSourceId The ID assigned to the DataSource during creation. This value should be identical to the value of the DataSourceID in the request. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Errors For information about the errors that are common to all actions, see Common Errors. InternalServerException An error on the server occurred when trying to process a request. HTTP Status Code: 500 InvalidInputException An error on the client occurred. Typically, the cause is an invalid input value. HTTP Status Code: 400 ResourceNotFoundException A specified resource cannot be located. Response Syntax API Version 2014-12-12 152 MachineLearning HTTP Status Code: 400 Examples API Reference The following is a sample request and response of the UpdateDataSource operation. This example illustrates one usage of UpdateDataSource. Sample Request POST / HTTP/1.1 Host: machinelearning.<region>.<domain> x-amz-Date: <Date> Authorization: AWS4-HMAC-SHA256 Credential=<Credential>, SignedHeaders=contenttype;date;host;user-agent;x-amz-date;x-amz-target;x-amzn- requestid,Signature=<Signature> User-Agent: <UserAgentString> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Connection: Keep-Alive X-Amz-Target: AmazonML_20141212.UpdateDataSource { "DataSourceId": "ds-exampleDataSourceId", "DataSourceName": "ds-exampleDataSourceName" } Sample Response HTTP/1.1 200 OK x-amzn-RequestId: <RequestId> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Date: <Date> {"DataSourceId": "ds-exampleDataSourceId"} See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: Examples API Version 2014-12-12 153 API Reference MachineLearning • AWS Command Line Interface • AWS SDK for .NET • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python • AWS SDK for Ruby V3 See Also API Version 2014-12-12 154 MachineLearning UpdateEvaluation Updates the EvaluationName of an Evaluation. API Reference You can use the GetEvaluation operation to view the contents of the updated data element. Request Syntax { "EvaluationId": "string", "EvaluationName": "string" } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. EvaluationId The ID assigned to the Evaluation during creation. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Required: Yes EvaluationName A new user-supplied name or description of the Evaluation that will replace the current content. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: Yes UpdateEvaluation API Version 2014-12-12 155 MachineLearning Response Syntax { "EvaluationId": "string" } Response Elements API Reference If the action is successful, the service |
amazonml-api-030 | amazonml-api.pdf | 30 | Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. EvaluationId The ID assigned to the Evaluation during creation. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Required: Yes EvaluationName A new user-supplied name or description of the Evaluation that will replace the current content. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: Yes UpdateEvaluation API Version 2014-12-12 155 MachineLearning Response Syntax { "EvaluationId": "string" } Response Elements API Reference If the action is successful, the service sends back an HTTP 200 response. The following data is returned in JSON format by the service. EvaluationId The ID assigned to the Evaluation during creation. This value should be identical to the value of the Evaluation in the request. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Errors For information about the errors that are common to all actions, see Common Errors. InternalServerException An error on the server occurred when trying to process a request. HTTP Status Code: 500 InvalidInputException An error on the client occurred. Typically, the cause is an invalid input value. HTTP Status Code: 400 ResourceNotFoundException A specified resource cannot be located. Response Syntax API Version 2014-12-12 156 MachineLearning HTTP Status Code: 400 Examples API Reference The following is a sample request and response of the UpdateEvaluation operation. This example illustrates one usage of UpdateEvaluation. Sample Request POST / HTTP/1.1 Host: machinelearning.<region>.<domain> x-amz-Date: <Date> Authorization: AWS4-HMAC-SHA256 Credential=<Credential>, SignedHeaders=contenttype;date;host;user-agent;x-amz-date;x-amz-target;x-amzn- requestid,Signature=<Signature> User-Agent: <UserAgentString> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Connection: Keep-Alive X-Amz-Target: AmazonML_20141212.UpdateEvaluation { "EvaluationId": "ev-exampleEvaluationId", "EvaluationName": "ev-exampleEvaluationName" } Sample Response HTTP/1.1 200 OK x-amzn-RequestId: <RequestId> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Date: <Date> {"EvaluationId": "ev-exampleEvaluationId"} See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: Examples API Version 2014-12-12 157 API Reference MachineLearning • AWS Command Line Interface • AWS SDK for .NET • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python • AWS SDK for Ruby V3 See Also API Version 2014-12-12 158 MachineLearning UpdateMLModel API Reference Updates the MLModelName and the ScoreThreshold of an MLModel. You can use the GetMLModel operation to view the contents of the updated data element. Request Syntax { "MLModelId": "string", "MLModelName": "string", "ScoreThreshold": number } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. MLModelId The ID assigned to the MLModel during creation. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Required: Yes MLModelName A user-supplied name or description of the MLModel. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No UpdateMLModel API Version 2014-12-12 159 MachineLearning ScoreThreshold API Reference The ScoreThreshold used in binary classification MLModel that marks the boundary between a positive prediction and a negative prediction. Output values greater than or equal to the ScoreThreshold receive a positive result from the MLModel, such as true. Output values less than the ScoreThreshold receive a negative response from the MLModel, such as false. Type: Float Required: No Response Syntax { "MLModelId": "string" } Response Elements If the action is successful, the service sends back an HTTP 200 response. The following data is returned in JSON format by the service. MLModelId The ID assigned to the MLModel during creation. This value should be identical to the value of the MLModelID in the request. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Errors For information about the errors that are common to all actions, see Common Errors. Response Syntax API Version 2014-12-12 160 MachineLearning InternalServerException API Reference An error on the server occurred when trying to process a request. HTTP Status Code: 500 InvalidInputException An error on the client occurred. Typically, the cause is an invalid input value. HTTP Status Code: 400 ResourceNotFoundException A specified resource cannot be located. HTTP Status Code: 400 Examples The following is a sample request and response of the UpdateMLModel operation. This example illustrates one usage of UpdateMLModel. Sample Request POST / HTTP/1.1 Host: machinelearning.<region>.<domain> x-amz-Date: <Date> Authorization: AWS4-HMAC-SHA256 Credential=<Credential>, SignedHeaders=contenttype;date;host;user-agent;x-amz-date;x-amz-target;x-amzn- requestid,Signature=<Signature> User-Agent: <UserAgentString> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Connection: Keep-Alive X-Amz-Target: AmazonML_20141212.UpdateMLModel { "MLModelId": "ml-exampleModelId", "MLModelName": "ml-exampleModelName", "ScoreThreshold": 0.8 } Examples API Version 2014-12-12 161 API Reference MachineLearning Sample Response HTTP/1.1 200 OK x-amzn-RequestId: <RequestId> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Date: <Date> {"MLModelId": "pr-exampleModelId"} See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS Command Line Interface • AWS SDK for .NET • AWS |
amazonml-api-031 | amazonml-api.pdf | 31 | request and response of the UpdateMLModel operation. This example illustrates one usage of UpdateMLModel. Sample Request POST / HTTP/1.1 Host: machinelearning.<region>.<domain> x-amz-Date: <Date> Authorization: AWS4-HMAC-SHA256 Credential=<Credential>, SignedHeaders=contenttype;date;host;user-agent;x-amz-date;x-amz-target;x-amzn- requestid,Signature=<Signature> User-Agent: <UserAgentString> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Connection: Keep-Alive X-Amz-Target: AmazonML_20141212.UpdateMLModel { "MLModelId": "ml-exampleModelId", "MLModelName": "ml-exampleModelName", "ScoreThreshold": 0.8 } Examples API Version 2014-12-12 161 API Reference MachineLearning Sample Response HTTP/1.1 200 OK x-amzn-RequestId: <RequestId> Content-Type: application/x-amz-json-1.1 Content-Length: <PayloadSizeBytes> Date: <Date> {"MLModelId": "pr-exampleModelId"} See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS Command Line Interface • AWS SDK for .NET • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python • AWS SDK for Ruby V3 See Also API Version 2014-12-12 162 MachineLearning Data Types API Reference The Amazon Machine Learning API contains several data types that various actions use. This section describes each data type in detail. Note The order of each element in a data type structure is not guaranteed. Applications should not assume a particular order. The following data types are supported: • BatchPrediction • DataSource • Evaluation • MLModel • PerformanceMetrics • Prediction • RDSDatabase • RDSDatabaseCredentials • RDSDataSpec • RDSMetadata • RealtimeEndpointInfo • RedshiftDatabase • RedshiftDatabaseCredentials • RedshiftDataSpec • RedshiftMetadata • S3DataSpec • Tag API Version 2014-12-12 163 MachineLearning BatchPrediction API Reference Represents the output of a GetBatchPrediction operation. The content consists of the detailed metadata, the status, and the data file information of a Batch Prediction. Contents BatchPredictionDataSourceId The ID of the DataSource that points to the group of observations to predict. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Required: No BatchPredictionId The ID assigned to the BatchPrediction at creation. This value should be identical to the value of the BatchPredictionID in the request. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Required: No ComputeTime Long integer type that is a 64-bit signed number. Type: Long Required: No CreatedAt The time that the BatchPrediction was created. The time is expressed in epoch time. BatchPrediction API Version 2014-12-12 164 MachineLearning Type: Timestamp Required: No CreatedByIamUser API Reference The AWS user account that invoked the BatchPrediction. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account. Type: String Pattern: arn:aws:iam::[0-9]+:((user/.+)|(root)) Required: No FinishedAt A timestamp represented in epoch time. Type: Timestamp Required: No InputDataLocationS3 The location of the data file or directory in Amazon Simple Storage Service (Amazon S3). Type: String Length Constraints: Maximum length of 2048. Pattern: s3://([^/]+)(/.*)? Required: No InvalidRecordCount Long integer type that is a 64-bit signed number. Type: Long Required: No LastUpdatedAt The time of the most recent edit to the BatchPrediction. The time is expressed in epoch time. Contents API Version 2014-12-12 165 MachineLearning Type: Timestamp Required: No Message API Reference A description of the most recent details about processing the batch prediction request. Type: String Length Constraints: Maximum length of 10240. Required: No MLModelId The ID of the MLModel that generated predictions for the BatchPrediction request. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Required: No Name A user-supplied name or description of the BatchPrediction. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No OutputUri The location of an Amazon S3 bucket or directory to receive the operation results. The following substrings are not allowed in the s3 key portion of the outputURI field: ':', '//', '/./', '/../'. Type: String Length Constraints: Maximum length of 2048. Contents API Version 2014-12-12 166 MachineLearning API Reference Pattern: s3://([^/]+)(/.*)? Required: No StartedAt A timestamp represented in epoch time. Type: Timestamp Required: No Status The status of the BatchPrediction. This element can have one of the following values: • PENDING - Amazon Machine Learning (Amazon ML) submitted a request to generate predictions for a batch of observations. • INPROGRESS - The process is underway. • FAILED - The request to perform a batch prediction did not run to completion. It is not usable. • COMPLETED - The batch prediction process completed successfully. • DELETED - The BatchPrediction is marked as deleted. It is not usable. Type: String Valid Values: PENDING | INPROGRESS | FAILED | COMPLETED | DELETED Required: No TotalRecordCount Long integer type that is a 64-bit signed number. Type: Long Required: No See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: See Also API Version 2014-12-12 167 MachineLearning • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 API Reference See Also API Version 2014-12-12 168 |
amazonml-api-032 | amazonml-api.pdf | 32 | - The batch prediction process completed successfully. • DELETED - The BatchPrediction is marked as deleted. It is not usable. Type: String Valid Values: PENDING | INPROGRESS | FAILED | COMPLETED | DELETED Required: No TotalRecordCount Long integer type that is a 64-bit signed number. Type: Long Required: No See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: See Also API Version 2014-12-12 167 MachineLearning • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 API Reference See Also API Version 2014-12-12 168 MachineLearning DataSource API Reference Represents the output of the GetDataSource operation. The content consists of the detailed metadata and data file information and the current status of the DataSource. Contents ComputeStatistics The parameter is true if statistics need to be generated from the observation data. Type: Boolean Required: No ComputeTime Long integer type that is a 64-bit signed number. Type: Long Required: No CreatedAt The time that the DataSource was created. The time is expressed in epoch time. Type: Timestamp Required: No CreatedByIamUser The AWS user account from which the DataSource was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account. Type: String Pattern: arn:aws:iam::[0-9]+:((user/.+)|(root)) Required: No DataSource API Version 2014-12-12 169 MachineLearning DataLocationS3 API Reference The location and name of the data in Amazon Simple Storage Service (Amazon S3) that is used by a DataSource. Type: String Length Constraints: Maximum length of 2048. Pattern: s3://([^/]+)(/.*)? Required: No DataRearrangement A JSON string that represents the splitting and rearrangement requirement used when this DataSource was created. Type: String Required: No DataSizeInBytes The total number of observations contained in the data files that the DataSource references. Type: Long Required: No DataSourceId The ID that is assigned to the DataSource during creation. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Required: No FinishedAt A timestamp represented in epoch time. Contents API Version 2014-12-12 170 MachineLearning Type: Timestamp Required: No LastUpdatedAt API Reference The time of the most recent edit to the BatchPrediction. The time is expressed in epoch time. Type: Timestamp Required: No Message A description of the most recent details about creating the DataSource. Type: String Length Constraints: Maximum length of 10240. Required: No Name A user-supplied name or description of the DataSource. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No NumberOfFiles The number of data files referenced by the DataSource. Type: Long Required: No RDSMetadata The datasource details that are specific to Amazon RDS. Contents API Version 2014-12-12 171 MachineLearning Type: RDSMetadata object Required: No RedshiftMetadata API Reference Describes the DataSource details specific to Amazon Redshift. Type: RedshiftMetadata object Required: No RoleARN The Amazon Resource Name (ARN) of an AWS IAM Role, such as the following: arn:aws:iam::account:role/rolename. Type: String Length Constraints: Minimum length of 1. Maximum length of 110. Required: No StartedAt A timestamp represented in epoch time. Type: Timestamp Required: No Status The current status of the DataSource. This element can have one of the following values: • PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create a DataSource. • INPROGRESS - The creation process is underway. • FAILED - The request to create a DataSource did not run to completion. It is not usable. • COMPLETED - The creation process completed successfully. • DELETED - The DataSource is marked as deleted. It is not usable. Type: String Valid Values: PENDING | INPROGRESS | FAILED | COMPLETED | DELETED Contents API Version 2014-12-12 172 MachineLearning Required: No See Also API Reference For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 See Also API Version 2014-12-12 173 MachineLearning Evaluation API Reference Represents the output of GetEvaluation operation. The content consists of the detailed metadata and data file information and the current status of the Evaluation. Contents ComputeTime Long integer type that is a 64-bit signed number. Type: Long Required: No CreatedAt The time that the Evaluation was created. The time is expressed in epoch time. Type: Timestamp Required: No CreatedByIamUser The AWS user account that invoked the evaluation. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account. Type: String Pattern: arn:aws:iam::[0-9]+:((user/.+)|(root)) Required: No EvaluationDataSourceId The ID of the DataSource that is used to evaluate the MLModel. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Evaluation API Version 2014-12-12 174 MachineLearning Required: No EvaluationId The ID that is assigned to the Evaluation at creation. Type: String Length Constraints: Minimum length of 1. Maximum length of |
amazonml-api-033 | amazonml-api.pdf | 33 | expressed in epoch time. Type: Timestamp Required: No CreatedByIamUser The AWS user account that invoked the evaluation. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account. Type: String Pattern: arn:aws:iam::[0-9]+:((user/.+)|(root)) Required: No EvaluationDataSourceId The ID of the DataSource that is used to evaluate the MLModel. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Evaluation API Version 2014-12-12 174 MachineLearning Required: No EvaluationId The ID that is assigned to the Evaluation at creation. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. API Reference Pattern: [a-zA-Z0-9_.-]+ Required: No FinishedAt A timestamp represented in epoch time. Type: Timestamp Required: No InputDataLocationS3 The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation. Type: String Length Constraints: Maximum length of 2048. Pattern: s3://([^/]+)(/.*)? Required: No LastUpdatedAt The time of the most recent edit to the Evaluation. The time is expressed in epoch time. Type: Timestamp Required: No Message A description of the most recent details about evaluating the MLModel. Contents API Version 2014-12-12 175 API Reference MachineLearning Type: String Length Constraints: Maximum length of 10240. Required: No MLModelId The ID of the MLModel that is the focus of the evaluation. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Required: No Name A user-supplied name or description of the Evaluation. Type: String Length Constraints: Maximum length of 1024. Pattern: .*\S.*|^$ Required: No PerformanceMetrics Measurements of how well the MLModel performed, using observations referenced by the DataSource. One of the following metrics is returned, based on the type of the MLModel: • BinaryAUC: A binary MLModel uses the Area Under the Curve (AUC) technique to measure performance. • RegressionRMSE: A regression MLModel uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable. • MulticlassAvgFScore: A multiclass MLModel uses the F1 score technique to measure performance. For more information about performance metrics, please see the Amazon Machine Learning Developer Guide. Contents API Version 2014-12-12 176 MachineLearning API Reference Type: PerformanceMetrics object Required: No StartedAt A timestamp represented in epoch time. Type: Timestamp Required: No Status The status of the evaluation. This element can have one of the following values: • PENDING - Amazon Machine Learning (Amazon ML) submitted a request to evaluate an MLModel. • INPROGRESS - The evaluation is underway. • FAILED - The request to evaluate an MLModel did not run to completion. It is not usable. • COMPLETED - The evaluation process completed successfully. • DELETED - The Evaluation is marked as deleted. It is not usable. Type: String Valid Values: PENDING | INPROGRESS | FAILED | COMPLETED | DELETED Required: No See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 See Also API Version 2014-12-12 177 MachineLearning MLModel API Reference Represents the output of a GetMLModel operation. The content consists of the detailed metadata and the current status of the MLModel. Contents Algorithm The algorithm used to train the MLModel. The following algorithm is supported: • SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function. Type: String Valid Values: sgd Required: No ComputeTime Long integer type that is a 64-bit signed number. Type: Long Required: No CreatedAt The time that the MLModel was created. The time is expressed in epoch time. Type: Timestamp Required: No CreatedByIamUser The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account. Type: String Pattern: arn:aws:iam::[0-9]+:((user/.+)|(root)) MLModel API Version 2014-12-12 178 API Reference MachineLearning Required: No EndpointInfo The current endpoint of the MLModel. Type: RealtimeEndpointInfo object Required: No FinishedAt A timestamp represented in epoch time. Type: Timestamp Required: No InputDataLocationS3 The location of the data file or directory in Amazon Simple Storage Service (Amazon S3). Type: String Length Constraints: Maximum length of 2048. Pattern: s3://([^/]+)(/.*)? Required: No LastUpdatedAt The time of the most recent edit to the MLModel. The time is expressed in epoch time. Type: Timestamp Required: No Message A description of the most recent details about accessing the MLModel. Type: String Length Constraints: Maximum length of 10240. Contents API Version 2014-12-12 179 API Reference MachineLearning Required: No MLModelId The ID assigned to the MLModel at creation. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Required: No MLModelType Identifies the MLModel category. The following are the available types: • REGRESSION - Produces a numeric result. For |
amazonml-api-034 | amazonml-api.pdf | 34 | Pattern: s3://([^/]+)(/.*)? Required: No LastUpdatedAt The time of the most recent edit to the MLModel. The time is expressed in epoch time. Type: Timestamp Required: No Message A description of the most recent details about accessing the MLModel. Type: String Length Constraints: Maximum length of 10240. Contents API Version 2014-12-12 179 API Reference MachineLearning Required: No MLModelId The ID assigned to the MLModel at creation. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Required: No MLModelType Identifies the MLModel category. The following are the available types: • REGRESSION - Produces a numeric result. For example, "What price should a house be listed at?" • BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?". • MULTICLASS - Produces one of several possible results. For example, "Is this a HIGH-, LOW-, or MEDIUM-risk trade?". Type: String Valid Values: REGRESSION | BINARY | MULTICLASS Required: No Name A user-supplied name or description of the MLModel. Type: String Length Constraints: Maximum length of 1024. Required: No ScoreThreshold The score threshold for the MLModel. Contents API Version 2014-12-12 180 MachineLearning Type: Float Required: No ScoreThresholdLastUpdatedAt API Reference The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time. Type: Timestamp Required: No SizeInBytes Long integer type that is a 64-bit signed number. Type: Long Required: No StartedAt A timestamp represented in epoch time. Type: Timestamp Required: No Status The current status of an MLModel. This element can have one of the following values: • PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel. • INPROGRESS - The creation process is underway. • FAILED - The request to create an MLModel didn't run to completion. The model isn't usable. • COMPLETED - The creation process completed successfully. • DELETED - The MLModel is marked as deleted. It isn't usable. Type: String Valid Values: PENDING | INPROGRESS | FAILED | COMPLETED | DELETED Contents API Version 2014-12-12 181 MachineLearning Required: No TrainingDataSourceId API Reference The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Pattern: [a-zA-Z0-9_.-]+ Required: No TrainingParameters A list of the training parameters in the MLModel. The list is implemented as a map of key-value pairs. The following is the current set of training parameters: • sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance. The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432. • sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 100. The default value is 10. • sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none. • sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08. The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2 is specified. Use this parameter sparingly. Contents API Version 2014-12-12 182 MachineLearning API Reference • sgd.l2RegularizationAmount - The coefficient regularization L2 norm, which controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08. The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1 is specified. Use this parameter sparingly. Type: String to string map Required: No See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 See Also API Version 2014-12-12 183 MachineLearning API Reference PerformanceMetrics Measurements of how well the MLModel performed on known observations. One of the following metrics is returned, based on the type of the MLModel: • BinaryAUC: The binary MLModel uses the Area Under the Curve (AUC) technique to measure performance. • RegressionRMSE: The regression MLModel uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable. • MulticlassAvgFScore: The multiclass MLModel uses the F1 score technique |
amazonml-api-035 | amazonml-api.pdf | 35 | AWS SDK for Java V2 • AWS SDK for Ruby V3 See Also API Version 2014-12-12 183 MachineLearning API Reference PerformanceMetrics Measurements of how well the MLModel performed on known observations. One of the following metrics is returned, based on the type of the MLModel: • BinaryAUC: The binary MLModel uses the Area Under the Curve (AUC) technique to measure performance. • RegressionRMSE: The regression MLModel uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable. • MulticlassAvgFScore: The multiclass MLModel uses the F1 score technique to measure performance. For more information about performance metrics, please see the Amazon Machine Learning Developer Guide. Contents Properties Specific performance metric information. Type: String to string map Required: No See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 PerformanceMetrics API Version 2014-12-12 184 MachineLearning Prediction The output from a Predict operation: API Reference • Details - Contains the following attributes: DetailsAttributes.PREDICTIVE_MODEL_TYPE - REGRESSION | BINARY | MULTICLASS DetailsAttributes.ALGORITHM - SGD • PredictedLabel - Present for either a BINARY or MULTICLASS MLModel request. • PredictedScores - Contains the raw classification score corresponding to each label. • PredictedValue - Present for a REGRESSION MLModel request. Contents details Provides any additional details regarding the prediction. Type: String to string map Valid Keys: PredictiveModelType | Algorithm Value Length Constraints: Minimum length of 1. Required: No predictedLabel The prediction label for either a BINARY or MULTICLASS MLModel. Type: String Length Constraints: Minimum length of 1. Required: No predictedScores Provides the raw classification score corresponding to each label. Type: String to float map Key Length Constraints: Minimum length of 1. Prediction API Version 2014-12-12 185 MachineLearning Required: No predictedValue The prediction value for REGRESSION MLModel. API Reference Type: Float Required: No See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 See Also API Version 2014-12-12 186 API Reference MachineLearning RDSDatabase The database details of an Amazon RDS database. Contents DatabaseName The name of a database hosted on an RDS DB instance. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Required: Yes InstanceIdentifier The ID of an RDS DB instance. Type: String Length Constraints: Minimum length of 1. Maximum length of 63. Required: Yes See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 RDSDatabase API Version 2014-12-12 187 MachineLearning API Reference RDSDatabaseCredentials The database credentials to connect to a database on an RDS DB instance. Contents Password The password to be used by Amazon ML to connect to a database on an RDS DB instance. The password should have sufficient permissions to execute the RDSSelectQuery query. Type: String Length Constraints: Minimum length of 8. Maximum length of 128. Required: Yes Username The username to be used by Amazon ML to connect to database on an Amazon RDS instance. The username should have sufficient permissions to execute an RDSSelectSqlQuery query. Type: String Length Constraints: Minimum length of 1. Maximum length of 128. Required: Yes See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 RDSDatabaseCredentials API Version 2014-12-12 188 MachineLearning RDSDataSpec API Reference The data specification of an Amazon Relational Database Service (Amazon RDS) DataSource. Contents DatabaseCredentials The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database. Type: RDSDatabaseCredentials object Required: Yes DatabaseInformation Describes the DatabaseName and InstanceIdentifier of an Amazon RDS database. Type: RDSDatabase object Required: Yes ResourceRole The role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute Cloud (Amazon EC2) instance to carry out the copy operation from Amazon RDS to an Amazon S3 task. For more information, see Role templates for data pipelines. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Required: Yes S3StagingLocation The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using SelectSqlQuery is stored in this location. Type: String Length Constraints: Maximum length of 2048. RDSDataSpec API Version 2014-12-12 189 MachineLearning API Reference Pattern: s3://([^/]+)(/.*)? Required: Yes SecurityGroupIds The security group IDs to be used to access a VPC-based RDS DB instance. Ensure that there are appropriate ingress rules set up to allow access to the RDS DB instance. This attribute is used by |
amazonml-api-036 | amazonml-api.pdf | 36 | see Role templates for data pipelines. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Required: Yes S3StagingLocation The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using SelectSqlQuery is stored in this location. Type: String Length Constraints: Maximum length of 2048. RDSDataSpec API Version 2014-12-12 189 MachineLearning API Reference Pattern: s3://([^/]+)(/.*)? Required: Yes SecurityGroupIds The security group IDs to be used to access a VPC-based RDS DB instance. Ensure that there are appropriate ingress rules set up to allow access to the RDS DB instance. This attribute is used by Data Pipeline to carry out the copy operation from Amazon RDS to an Amazon S3 task. Type: Array of strings Length Constraints: Minimum length of 1. Maximum length of 255. Required: Yes SelectSqlQuery The query that is used to retrieve the observation data for the DataSource. Type: String Length Constraints: Minimum length of 1. Maximum length of 16777216. Required: Yes ServiceRole The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Required: Yes SubnetId The subnet ID to be used to access a VPC-based RDS DB instance. This attribute is used by Data Pipeline to carry out the copy task from Amazon RDS to Amazon S3. Type: String Contents API Version 2014-12-12 190 MachineLearning API Reference Length Constraints: Minimum length of 1. Maximum length of 255. Required: Yes DataRearrangement A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource. There are multiple parameters that control what data is used to create a datasource: • percentBegin Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource. • percentEnd Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource. • complement The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter. For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data. Datasource for evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}} Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}} • strategy To change how Amazon ML splits the data for a datasource, use the strategy parameter. Contents API Version 2014-12-12 191 MachineLearning API Reference The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data. The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources: Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}} Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}} To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo- random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non- similar data records. The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources: Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "strategyParams": {"randomSeed":"RANDOMSEED"}}} Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "strategyParams": {"randomSeed":"RANDOMSEED"}, "complement":"true"}} Type: String Required: No Contents API Version 2014-12-12 192 MachineLearning DataSchema |
amazonml-api-037 | amazonml-api.pdf | 37 | the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non- similar data records. The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources: Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "strategyParams": {"randomSeed":"RANDOMSEED"}}} Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "strategyParams": {"randomSeed":"RANDOMSEED"}, "complement":"true"}} Type: String Required: No Contents API Version 2014-12-12 192 MachineLearning DataSchema API Reference A JSON string that represents the schema for an Amazon RDS DataSource. The DataSchema defines the structure of the observation data in the data file(s) referenced in the DataSource. A DataSchema is not required if you specify a DataSchemaUri Define your DataSchema as a series of key-value pairs. attributes and excludedAttributeNames have an array of key-value pairs for their value. Use the following format to define your DataSchema. { "version": "1.0", "recordAnnotationFieldName": "F1", "recordWeightFieldName": "F2", "targetAttributeName": "F3", "dataFormat": "CSV", "dataFileContainsHeader": true, "attributes": [ { "attributeName": "F1", "attributeType": "TEXT" }, { "attributeName": "F2", "attributeType": "NUMERIC" }, { "attributeName": "F3", "attributeType": "CATEGORICAL" }, { "attributeName": "F4", "attributeType": "NUMERIC" }, { "attributeName": "F5", "attributeType": "CATEGORICAL" }, { "attributeName": "F6", "attributeType": "TEXT" }, { "attributeName": "F7", "attributeType": "WEIGHTED_INT_SEQUENCE" }, { "attributeName": "F8", "attributeType": "WEIGHTED_STRING_SEQUENCE" } ], "excludedAttributeNames": [ "F6" ] } Type: String Length Constraints: Maximum length of 131071. Required: No DataSchemaUri The Amazon S3 location of the DataSchema. Contents API Version 2014-12-12 193 MachineLearning Type: String Length Constraints: Maximum length of 2048. Pattern: s3://([^/]+)(/.*)? Required: No See Also API Reference For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 See Also API Version 2014-12-12 194 MachineLearning RDSMetadata The datasource details that are specific to Amazon RDS. API Reference Contents Database The database details required to connect to an Amazon RDS. Type: RDSDatabase object Required: No DatabaseUserName The username to be used by Amazon ML to connect to database on an Amazon RDS instance. The username should have sufficient permissions to execute an RDSSelectSqlQuery query. Type: String Length Constraints: Minimum length of 1. Maximum length of 128. Required: No DataPipelineId The ID of the Data Pipeline instance that is used to carry to copy data from Amazon RDS to Amazon S3. You can use the ID to find details about the instance in the Data Pipeline console. Type: String Length Constraints: Minimum length of 1. Maximum length of 1024. Required: No ResourceRole The role (DataPipelineDefaultResourceRole) assumed by an Amazon EC2 instance to carry out the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines. Type: String RDSMetadata API Version 2014-12-12 195 MachineLearning API Reference Length Constraints: Minimum length of 1. Maximum length of 64. Required: No SelectSqlQuery The SQL query that is supplied during CreateDataSourceFromRDS. Returns only if Verbose is true in GetDataSourceInput. Type: String Length Constraints: Minimum length of 1. Maximum length of 16777216. Required: No ServiceRole The role (DataPipelineDefaultRole) assumed by the Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines. Type: String Length Constraints: Minimum length of 1. Maximum length of 64. Required: No See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 See Also API Version 2014-12-12 196 MachineLearning API Reference RealtimeEndpointInfo Describes the real-time endpoint information for an MLModel. Contents CreatedAt The time that the request to create the real-time endpoint for the MLModel was received. The time is expressed in epoch time. Type: Timestamp Required: No EndpointStatus The current status of the real-time endpoint for the MLModel. This element can have one of the following values: • NONE - Endpoint does not exist or was previously deleted. • READY - Endpoint is ready to be used for real-time predictions. • UPDATING - Updating/creating the endpoint. Type: String Valid Values: NONE | READY | UPDATING | FAILED Required: No EndpointUrl The URI that specifies where to send real-time prediction requests for the MLModel. Note: The application must wait until the real-time endpoint is ready before using this URI. Type: String Length Constraints: Maximum length of 2048. Pattern: https://[a-zA-Z0-9-.]*\.amazon(aws)?\.com[/]? Required: No RealtimeEndpointInfo API Version 2014-12-12 197 MachineLearning PeakRequestsPerSecond API Reference The maximum processing rate for the real-time endpoint for MLModel, measured in incoming requests per second. Type: Integer Required: No See Also For more information about using this API in one |
amazonml-api-038 | amazonml-api.pdf | 38 | • UPDATING - Updating/creating the endpoint. Type: String Valid Values: NONE | READY | UPDATING | FAILED Required: No EndpointUrl The URI that specifies where to send real-time prediction requests for the MLModel. Note: The application must wait until the real-time endpoint is ready before using this URI. Type: String Length Constraints: Maximum length of 2048. Pattern: https://[a-zA-Z0-9-.]*\.amazon(aws)?\.com[/]? Required: No RealtimeEndpointInfo API Version 2014-12-12 197 MachineLearning PeakRequestsPerSecond API Reference The maximum processing rate for the real-time endpoint for MLModel, measured in incoming requests per second. Type: Integer Required: No See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 See Also API Version 2014-12-12 198 MachineLearning RedshiftDatabase API Reference Describes the database details required to connect to an Amazon Redshift database. Contents ClusterIdentifier The ID of an Amazon Redshift cluster. Type: String Length Constraints: Minimum length of 1. Maximum length of 63. Required: Yes DatabaseName The name of a database hosted on an Amazon Redshift cluster. Type: String Length Constraints: Minimum length of 1. Maximum length of 127. Required: Yes See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 RedshiftDatabase API Version 2014-12-12 199 MachineLearning API Reference RedshiftDatabaseCredentials Describes the database credentials for connecting to a database on an Amazon Redshift cluster. Contents Password A password to be used by Amazon ML to connect to a database on an Amazon Redshift cluster. The password should have sufficient permissions to execute a RedshiftSelectSqlQuery query. The password should be valid for an Amazon Redshift USER. Type: String Length Constraints: Minimum length of 8. Maximum length of 64. Required: Yes Username A username to be used by Amazon Machine Learning (Amazon ML)to connect to a database on an Amazon Redshift cluster. The username should have sufficient permissions to execute the RedshiftSelectSqlQuery query. The username should be valid for an Amazon Redshift USER. Type: String Length Constraints: Minimum length of 1. Maximum length of 128. Required: Yes See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 RedshiftDatabaseCredentials API Version 2014-12-12 200 MachineLearning RedshiftDataSpec API Reference Describes the data specification of an Amazon Redshift DataSource. Contents DatabaseCredentials Describes AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon Redshift database. Type: RedshiftDatabaseCredentials object Required: Yes DatabaseInformation Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource. Type: RedshiftDatabase object Required: Yes S3StagingLocation Describes an Amazon S3 location to store the result set of the SelectSqlQuery query. Type: String Length Constraints: Maximum length of 2048. Pattern: s3://([^/]+)(/.*)? Required: Yes SelectSqlQuery Describes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift DataSource. Type: String Length Constraints: Minimum length of 1. Maximum length of 16777216. RedshiftDataSpec API Version 2014-12-12 201 MachineLearning Required: Yes DataRearrangement API Reference A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource. There are multiple parameters that control what data is used to create a datasource: • percentBegin Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource. • percentEnd Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource. • complement The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter. For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data. Datasource for evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}} Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}} • strategy To change how Amazon ML splits the data for a datasource, use the strategy parameter. Contents API Version 2014-12-12 202 MachineLearning API Reference The default value for the strategy parameter is sequential, meaning that Amazon |
amazonml-api-039 | amazonml-api.pdf | 39 | the same values for percentBegin and percentEnd, along with the complement parameter. For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data. Datasource for evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}} Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}} • strategy To change how Amazon ML splits the data for a datasource, use the strategy parameter. Contents API Version 2014-12-12 202 MachineLearning API Reference The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data. The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources: Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}} Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}} To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo- random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non- similar data records. The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources: Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "strategyParams": {"randomSeed":"RANDOMSEED"}}} Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "strategyParams": {"randomSeed":"RANDOMSEED"}, "complement":"true"}} Type: String Required: No Contents API Version 2014-12-12 203 MachineLearning DataSchema API Reference A JSON string that represents the schema for an Amazon Redshift DataSource. The DataSchema defines the structure of the observation data in the data file(s) referenced in the DataSource. A DataSchema is not required if you specify a DataSchemaUri. Define your DataSchema as a series of key-value pairs. attributes and excludedAttributeNames have an array of key-value pairs for their value. Use the following format to define your DataSchema. { "version": "1.0", "recordAnnotationFieldName": "F1", "recordWeightFieldName": "F2", "targetAttributeName": "F3", "dataFormat": "CSV", "dataFileContainsHeader": true, "attributes": [ { "attributeName": "F1", "attributeType": "TEXT" }, { "attributeName": "F2", "attributeType": "NUMERIC" }, { "attributeName": "F3", "attributeType": "CATEGORICAL" }, { "attributeName": "F4", "attributeType": "NUMERIC" }, { "attributeName": "F5", "attributeType": "CATEGORICAL" }, { "attributeName": "F6", "attributeType": "TEXT" }, { "attributeName": "F7", "attributeType": "WEIGHTED_INT_SEQUENCE" }, { "attributeName": "F8", "attributeType": "WEIGHTED_STRING_SEQUENCE" } ], "excludedAttributeNames": [ "F6" ] } Type: String Length Constraints: Maximum length of 131071. Required: No DataSchemaUri Describes the schema location for an Amazon Redshift DataSource. Contents API Version 2014-12-12 204 MachineLearning Type: String Length Constraints: Maximum length of 2048. Pattern: s3://([^/]+)(/.*)? Required: No See Also API Reference For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 See Also API Version 2014-12-12 205 MachineLearning RedshiftMetadata Describes the DataSource details specific to Amazon Redshift. API Reference Contents DatabaseUserName A username to be used by Amazon Machine Learning (Amazon ML)to connect to a database on an Amazon Redshift cluster. The username should have sufficient permissions to execute the RedshiftSelectSqlQuery query. The username should be valid for an Amazon Redshift USER. Type: String Length Constraints: Minimum length of 1. Maximum length of 128. Required: No RedshiftDatabase Describes the database details required to connect to an Amazon Redshift database. Type: RedshiftDatabase object Required: No SelectSqlQuery The SQL query that is specified during CreateDataSourceFromRedshift. Returns only if Verbose is true in GetDataSourceInput. Type: String Length Constraints: Minimum length of 1. Maximum length of 16777216. Required: No See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: RedshiftMetadata API Version 2014-12-12 206 MachineLearning • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 API Reference See Also API Version 2014-12-12 207 MachineLearning S3DataSpec Describes the data specification of a DataSource. Contents DataLocationS3 API Reference The location of the data file(s) used by a DataSource. The URI specifies a data file or an Amazon Simple Storage Service (Amazon S3) directory or bucket containing data files. Type: String Length Constraints: |
amazonml-api-040 | amazonml-api.pdf | 40 | 16777216. Required: No See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: RedshiftMetadata API Version 2014-12-12 206 MachineLearning • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 API Reference See Also API Version 2014-12-12 207 MachineLearning S3DataSpec Describes the data specification of a DataSource. Contents DataLocationS3 API Reference The location of the data file(s) used by a DataSource. The URI specifies a data file or an Amazon Simple Storage Service (Amazon S3) directory or bucket containing data files. Type: String Length Constraints: Maximum length of 2048. Pattern: s3://([^/]+)(/.*)? Required: Yes DataRearrangement A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource. There are multiple parameters that control what data is used to create a datasource: • percentBegin Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource. • percentEnd Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource. • complement The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement S3DataSpec API Version 2014-12-12 208 MachineLearning API Reference parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter. For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data. Datasource for evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}} Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}} • strategy To change how Amazon ML splits the data for a datasource, use the strategy parameter. The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data. The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources: Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}} Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}} To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo- random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort Contents API Version 2014-12-12 209 MachineLearning API Reference order, which would otherwise result in training and evaluation datasources containing non- similar data records. The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources: Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "strategyParams": { "randomSeed":"RANDOMSEED"}}} Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "strategyParams": {"randomSeed":"RANDOMSEED"}, "complement":"true"}} Type: String Required: No DataSchema A JSON string that represents the schema for an Amazon S3 DataSource. The DataSchema defines the structure of the observation data in the data file(s) referenced in the DataSource. You must provide either the DataSchema or the DataSchemaLocationS3. Define your DataSchema as a series of key-value pairs. attributes and excludedAttributeNames have an array of key-value pairs for their value. Use the following format to define your DataSchema. { "version": "1.0", "recordAnnotationFieldName": "F1", "recordWeightFieldName": "F2", "targetAttributeName": "F3", "dataFormat": "CSV", "dataFileContainsHeader": true, "attributes": [ { "attributeName": "F1", "attributeType": "TEXT" }, { "attributeName": "F2", "attributeType": "NUMERIC" }, { "attributeName": "F3", "attributeType": "CATEGORICAL" }, { "attributeName": Contents API Version 2014-12-12 210 MachineLearning API Reference "F4", "attributeType": "NUMERIC" }, { "attributeName": "F5", "attributeType": "CATEGORICAL" }, { "attributeName": "F6", "attributeType": "TEXT" }, { "attributeName": "F7", "attributeType": "WEIGHTED_INT_SEQUENCE" }, { "attributeName": "F8", "attributeType": "WEIGHTED_STRING_SEQUENCE" } ], "excludedAttributeNames": [ "F6" ] } Type: String Length Constraints: Maximum length of |
amazonml-api-041 | amazonml-api.pdf | 41 | an array of key-value pairs for their value. Use the following format to define your DataSchema. { "version": "1.0", "recordAnnotationFieldName": "F1", "recordWeightFieldName": "F2", "targetAttributeName": "F3", "dataFormat": "CSV", "dataFileContainsHeader": true, "attributes": [ { "attributeName": "F1", "attributeType": "TEXT" }, { "attributeName": "F2", "attributeType": "NUMERIC" }, { "attributeName": "F3", "attributeType": "CATEGORICAL" }, { "attributeName": Contents API Version 2014-12-12 210 MachineLearning API Reference "F4", "attributeType": "NUMERIC" }, { "attributeName": "F5", "attributeType": "CATEGORICAL" }, { "attributeName": "F6", "attributeType": "TEXT" }, { "attributeName": "F7", "attributeType": "WEIGHTED_INT_SEQUENCE" }, { "attributeName": "F8", "attributeType": "WEIGHTED_STRING_SEQUENCE" } ], "excludedAttributeNames": [ "F6" ] } Type: String Length Constraints: Maximum length of 131071. Required: No DataSchemaLocationS3 Describes the schema location in Amazon S3. You must provide either the DataSchema or the DataSchemaLocationS3. Type: String Length Constraints: Maximum length of 2048. Pattern: s3://([^/]+)(/.*)? Required: No See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 See Also API Version 2014-12-12 211 MachineLearning Tag API Reference A custom key-value pair associated with an ML object, such as an ML model. Contents Key A unique identifier for the tag. Valid characters include Unicode letters, digits, white space, _, ., /, =, +, -, %, and @. Type: String Length Constraints: Minimum length of 1. Maximum length of 128. Pattern: ^([\p{L}\p{Z}\p{N}_.:/=+\-@]*)$ Required: No Value An optional string, typically used to describe or define the tag. Valid characters include Unicode letters, digits, white space, _, ., /, =, +, -, %, and @. Type: String Length Constraints: Minimum length of 0. Maximum length of 256. Pattern: ^([\p{L}\p{Z}\p{N}_.:/=+\-@]*)$ Required: No See Also For more information about using this API in one of the language-specific AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 Tag API Version 2014-12-12 212 MachineLearning API Reference Common Parameters The following list contains the parameters that all actions use for signing Signature Version 4 requests with a query string. Any action-specific parameters are listed in the topic for that action. For more information about Signature Version 4, see Signing AWS API requests in the IAM User Guide. Action The action to be performed. Type: string Required: Yes Version The API version that the request is written for, expressed in the format YYYY-MM-DD. Type: string Required: Yes X-Amz-Algorithm The hash algorithm that you used to create the request signature. Condition: Specify this parameter when you include authentication information in a query string instead of in the HTTP authorization header. Type: string Valid Values: AWS4-HMAC-SHA256 Required: Conditional X-Amz-Credential The credential scope value, which is a string that includes your access key, the date, the region you are targeting, the service you are requesting, and a termination string ("aws4_request"). The value is expressed in the following format: access_key/YYYYMMDD/region/service/ aws4_request. API Version 2014-12-12 213 MachineLearning API Reference For more information, see Create a signed AWS API request in the IAM User Guide. Condition: Specify this parameter when you include authentication information in a query string instead of in the HTTP authorization header. Type: string Required: Conditional X-Amz-Date The date that is used to create the signature. The format must be ISO 8601 basic format (YYYYMMDD'T'HHMMSS'Z'). For example, the following date time is a valid X-Amz-Date value: 20120325T120000Z. Condition: X-Amz-Date is optional for all requests; it can be used to override the date used for signing requests. If the Date header is specified in the ISO 8601 basic format, X-Amz-Date is not required. When X-Amz-Date is used, it always overrides the value of the Date header. For more information, see Elements of an AWS API request signature in the IAM User Guide. Type: string Required: Conditional X-Amz-Security-Token The temporary security token that was obtained through a call to AWS Security Token Service (AWS STS). For a list of services that support temporary security credentials from AWS STS, see AWS services that work with IAM in the IAM User Guide. Condition: If you're using temporary security credentials from AWS STS, you must include the security token. Type: string Required: Conditional X-Amz-Signature Specifies the hex-encoded signature that was calculated from the string to sign and the derived signing key. Condition: Specify this parameter when you include authentication information in a query string instead of in the HTTP authorization header. API Version 2014-12-12 214 MachineLearning Type: string Required: Conditional X-Amz-SignedHeaders API Reference Specifies all the HTTP headers that were included as part of the canonical request. For more information about specifying signed headers, see Create a signed AWS API request in the IAM User Guide. Condition: Specify this parameter when you include authentication information in a query string instead of in the HTTP authorization header. Type: string Required: Conditional API Version 2014-12-12 215 |
amazonml-api-042 | amazonml-api.pdf | 42 | to sign and the derived signing key. Condition: Specify this parameter when you include authentication information in a query string instead of in the HTTP authorization header. API Version 2014-12-12 214 MachineLearning Type: string Required: Conditional X-Amz-SignedHeaders API Reference Specifies all the HTTP headers that were included as part of the canonical request. For more information about specifying signed headers, see Create a signed AWS API request in the IAM User Guide. Condition: Specify this parameter when you include authentication information in a query string instead of in the HTTP authorization header. Type: string Required: Conditional API Version 2014-12-12 215 MachineLearning API Reference Common Errors This section lists the errors common to the API actions of all AWS services. For errors specific to an API action for this service, see the topic for that API action. AccessDeniedException You do not have sufficient access to perform this action. HTTP Status Code: 400 IncompleteSignature The request signature does not conform to AWS standards. HTTP Status Code: 400 InternalFailure The request processing has failed because of an unknown error, exception or failure. HTTP Status Code: 500 InvalidAction The action or operation requested is invalid. Verify that the action is typed correctly. HTTP Status Code: 400 InvalidClientTokenId The X.509 certificate or AWS access key ID provided does not exist in our records. HTTP Status Code: 403 NotAuthorized You do not have permission to perform this action. HTTP Status Code: 400 OptInRequired The AWS access key ID needs a subscription for the service. HTTP Status Code: 403 API Version 2014-12-12 216 MachineLearning RequestExpired API Reference The request reached the service more than 15 minutes after the date stamp on the request or more than 15 minutes after the request expiration date (such as for pre-signed URLs), or the date stamp on the request is more than 15 minutes in the future. HTTP Status Code: 400 ServiceUnavailable The request has failed due to a temporary failure of the server. HTTP Status Code: 503 ThrottlingException The request was denied due to request throttling. HTTP Status Code: 400 ValidationError The input fails to satisfy the constraints specified by an AWS service. HTTP Status Code: 400 API Version 2014-12-12 217 |
AmazonMonitronUG-001 | AmazonMonitronUG.pdf | 1 | User Guide Amazon Monitron Copyright © 2025 Amazon Web Services, Inc. and/or its affiliates. All rights reserved. Amazon Monitron User Guide Amazon Monitron: User Guide Copyright © 2025 Amazon Web Services, Inc. and/or its affiliates. All rights reserved. Amazon's trademarks and trade dress may not be used in connection with any product or service that is not Amazon's, in any manner that is likely to cause confusion among customers, or in any manner that disparages or discredits Amazon. All other trademarks not owned by Amazon are the property of their respective owners, who may or may not be affiliated with, connected to, or sponsored by Amazon. Amazon Monitron Table of Contents User Guide .......................................................................................................................................................... x What is Amazon Monitron? ............................................................................................................. 1 Amazon Monitron devices .......................................................................................................................... 1 Amazon Monitron software ........................................................................................................................ 3 Benefits of Amazon Monitron .................................................................................................................... 7 Pricing for Amazon Monitron .................................................................................................................... 7 Related resources .......................................................................................................................................... 7 Are you a first-time user of Amazon Monitron? .................................................................................... 8 How Amazon Monitron works ...................................................................................................... 12 Amazon Monitron workflow .................................................................................................................... 12 Amazon Monitron concepts ..................................................................................................................... 13 Amazon Monitron components ............................................................................................................... 17 Amazon Monitron alerts ........................................................................................................................... 22 Getting started .............................................................................................................................. 25 Setting up a project .................................................................................................................................. 25 Step 1: Create an account .................................................................................................................. 26 Step 2: Create a project ...................................................................................................................... 28 Step 3: Create admin users ................................................................................................................ 29 Step 4: (optional) Add Amazon Monitron users to your project ................................................. 30 Step 5: Invite users to your project .................................................................................................. 34 Adding assets and installing devices ..................................................................................................... 35 Step 1: Add a Gateway ........................................................................................................................ 35 Step 2: Adding Assets .......................................................................................................................... 40 Step 3: Attach Sensors ........................................................................................................................ 47 Step 4: Pairing Sensors to an Asset .................................................................................................. 49 Understanding warnings and alerts ....................................................................................................... 52 Step 1: Understanding asset health ................................................................................................. 53 Step 2: Viewing asset conditions ....................................................................................................... 57 Step 3: Viewing and acknowledging a machine abnormality ...................................................... 59 Step 4: Resolving a machine abnormality ....................................................................................... 63 Step 5: Muting and unmuting alerts ................................................................................................ 63 Projects ........................................................................................................................................... 75 Creating a project ...................................................................................................................................... 75 Using tags with your project ................................................................................................................... 76 iii Amazon Monitron User Guide Adding a tag to a project when you create it ................................................................................ 77 Adding a tag to a project after it’s been created .......................................................................... 79 Modifying or removing a tag ............................................................................................................. 80 Updating a project ..................................................................................................................................... 81 Switching between projects ..................................................................................................................... 82 Switching between projects in the web app ................................................................................... 82 Switching between projects in the mobile app .............................................................................. 85 Deleting a project ...................................................................................................................................... 89 Additional project tasks ............................................................................................................................ 90 Sites ................................................................................................................................................ 92 Organizing a project into sites ................................................................................................................ 92 Controlling access to projects and sites ................................................................................................ 93 Creating a site ............................................................................................................................................. 93 To add a site using the mobile app .................................................................................................. 93 To add a new site using the web app .............................................................................................. 94 Changing a site name ............................................................................................................................... 95 To change a site name using the mobile app ................................................................................ 95 To change a site name using the web app ..................................................................................... 95 Deleting a site ............................................................................................................................................. 96 To delete a site using the mobile app ............................................................................................. 96 To delete a site using the web app .................................................................................................. 97 Navigating between projects and sites in the mobile app ................................................................ 98 Switching from project level to site level ........................................................................................ 98 Switching from site level to project level ........................................................................................ 99 Gateways ...................................................................................................................................... 101 Ethernet gateways ................................................................................................................................... 101 Reading the LED lights on an Ethernet gateway ......................................................................... 103 Placing and installing an Ethernet gateway ................................................................................. 105 Commissioning an Ethernet gateway ............................................................................................. 110 Troubleshooting Ethernet gateway detection .............................................................................. 113 ................................................................................................................................................................ 115 Resetting the Ethernet gateway to factory settings ................................................................... 115 Viewing the list of gateways ............................................................................................................ 116 Viewing Ethernet gateway details .................................................................................................. 118 Editing Ethernet gateway name ...................................................................................................... 122 Deleting an Ethernet gateway ......................................................................................................... 126 iv Amazon Monitron User Guide Retrieving MAC address details ....................................................................................................... 127 Wi-Fi gateways ......................................................................................................................................... 132 Reading the LED lights on a Wi-Fi gateway ................................................................................. 133 Placing and installing a Wi-Fi gateway .......................................................................................... 135 Commissioning a Wi-Fi gateway ..................................................................................................... 143 Troubleshooting Wi-Fi gateway detection .................................................................................... 145 Troubleshooting Bluetooth pairing ................................................................................................. 147 Resetting the Wi-Fi gateway to factory settings ......................................................................... 147 Viewing the list of gateways ............................................................................................................ 148 Viewing Wi-Fi gateway details ......................................................................................................... 150 Editing Wi-Fi gateway name ............................................................................................................ 154 Deleting a Wi-Fi gateway ................................................................................................................. 158 Retrieving MAC address details ....................................................................................................... 159 Assets ............................................................................................................................................ 165 Creating asset classes ............................................................................................................................. 166 Creating a custom class .................................................................................................................... 167 Updating a custom class ................................................................................................................... 171 Deleting a custom class .................................................................................................................... 174 Managing assets ....................................................................................................................................... 178 Viewing the list of assets ....................................................................................................................... 180 To open the Assets list ...................................................................................................................... 180 Adding an asset ........................................................................................................................................ 180 Adding assets using the mobile app ................................................................................................ 41 Adding assets using the web app ..................................................................................................... 45 Changing an asset name ........................................................................................................................ 186 To change an asset's name in the mobile app ............................................................................. 186 To change an asset's name in the web app .................................................................................. 186 Moving an asset ....................................................................................................................................... 187 To move an asset on the web app |
AmazonMonitronUG-002 | AmazonMonitronUG.pdf | 2 | ............................................................................................................................. 166 Creating a custom class .................................................................................................................... 167 Updating a custom class ................................................................................................................... 171 Deleting a custom class .................................................................................................................... 174 Managing assets ....................................................................................................................................... 178 Viewing the list of assets ....................................................................................................................... 180 To open the Assets list ...................................................................................................................... 180 Adding an asset ........................................................................................................................................ 180 Adding assets using the mobile app ................................................................................................ 41 Adding assets using the web app ..................................................................................................... 45 Changing an asset name ........................................................................................................................ 186 To change an asset's name in the mobile app ............................................................................. 186 To change an asset's name in the web app .................................................................................. 186 Moving an asset ....................................................................................................................................... 187 To move an asset on the web app ................................................................................................. 188 To move an asset on the mobile app ............................................................................................ 190 Deleting an asset ..................................................................................................................................... 196 To delete an asset .............................................................................................................................. 196 Sensors ......................................................................................................................................... 198 Positioning a sensor ................................................................................................................................ 198 Mounting a sensor ................................................................................................................................... 202 Adding a sensor position ....................................................................................................................... 204 v Amazon Monitron User Guide To add a sensor position on the web app .................................................................................... 205 To add a sensor position on the mobile app ................................................................................ 206 Pairing a sensor to an asset .................................................................................................................. 212 To pair a sensor to an asset ............................................................................................................. 212 Renaming a sensor position .................................................................................................................. 218 Renaming a sensor position on the mobile app .......................................................................... 219 Renaming a sensor position on the web app ............................................................................... 219 Editing machine class .............................................................................................................................. 220 To edit machine class on the mobile app ..................................................................................... 221 To edit machine class on the web app .......................................................................................... 227 To edit machine class from the position detail page .................................................................. 228 Deleting a sensor ..................................................................................................................................... 228 To delete a sensor in the mobile app ............................................................................................ 229 To delete a sensor in the web app ................................................................................................. 230 Deleting a sensor position ..................................................................................................................... 231 To delete a sensor position in the mobile app ............................................................................ 231 To delete a sensor position in the web app ................................................................................. 232 Understanding sensor details ................................................................................................................ 233 Viewing sensor details ....................................................................................................................... 234 Sensor connectivity status ................................................................................................................ 236 Sensor battery status ........................................................................................................................ 237 Identifying sensor position .................................................................................................................... 239 Identifying paired sensor .................................................................................................................. 239 Missing or unread sensor .................................................................................................................. 243 Permissions and site commissioning issues .................................................................................. 244 Scanning sensor from another site ................................................................................................ 246 Ex-rated sensors ....................................................................................................................................... 247 Measurements and machine abnormalities ............................................................................... 251 Choosing your measurement viewing platform ................................................................................ 251 In-app updates .................................................................................................................................... 252 Viewing sensor measurements ............................................................................................................. 256 Understanding sensor measurements ................................................................................................. 258 Understanding asset status ................................................................................................................... 262 The Assets list ..................................................................................................................................... 262 Asset and position status ................................................................................................................. 264 Notifications ......................................................................................................................................... 267 vi Amazon Monitron User Guide Acknowledging a machine abnormality .............................................................................................. 269 To view and acknowledge a machine abnormality ..................................................................... 269 Resolving an abnormality ...................................................................................................................... 271 Failure modes ...................................................................................................................................... 271 Failure causes ...................................................................................................................................... 272 To resolve a machine abnormality using the mobile app .......................................................... 272 Taking a one-time measurement ......................................................................................................... 273 To take a one-time measurement (mobile app only) ................................................................. 274 Managing users ............................................................................................................................ 289 Managing admin users ............................................................................................................................ 289 User directory setup .......................................................................................................................... 290 Adding users as an admin ................................................................................................................ 299 Managing users as an admin user .................................................................................................. 301 Removing an admin user .................................................................................................................. 305 Sending an email invitation ............................................................................................................. 306 Managing non-admin users ................................................................................................................... 307 Displaying a list of users ................................................................................................................... 308 Adding a user ...................................................................................................................................... 310 Changing a user role ......................................................................................................................... 314 Removing a user ................................................................................................................................. 316 Networking .................................................................................................................................. 318 Networking with your mobile device .................................................................................................. 318 Setting up your Monitron network foundation with your mobile app .................................... 318 Setting up your gateways ................................................................................................................. 319 Setting up your sensors .................................................................................................................... 319 Securing your network ........................................................................................................................... 320 Accessing your data ..................................................................................................................... 322 Exporting your data to Amazon S3 ..................................................................................................... 322 Prerequisites ........................................................................................................................................ 323 Exporting your data with AWS CloudFormation (recommended option) ................................ 323 Exporting your data with the console ........................................................................................... 330 Exporting your data with CloudShell ............................................................................................. 350 Exporting your data with Kinesis v1 .................................................................................................... 360 Exporting your data to a Kinesis stream ....................................................................................... 360 Editing live data export settings ..................................................................................................... 361 Stopping a live data export ............................................................................................................. 361 vii Amazon Monitron User Guide Viewing data export errors ............................................................................................................... 362 Using server-side encryption for the Kinesis stream .................................................................. 362 Monitoring with Amazon CloudWatch Logs ................................................................................. 362 Storing exported data in Amazon S3 ............................................................................................. 364 Processing data with Lambda .......................................................................................................... 366 Understanding the v1 data export schema .................................................................................. 372 Exporting your data with Kinesis v2 .................................................................................................... 379 Exporting your data to a Kinesis stream ....................................................................................... 380 Editing live data export settings ..................................................................................................... 380 Stopping a live data export ............................................................................................................. 381 Viewing data export errors ............................................................................................................... 381 Using server-side encryption for the Kinesis stream .................................................................. 381 Monitoring with Amazon CloudWatch Logs ................................................................................. 382 Storing exported data in Amazon S3 ............................................................................................. 384 Processing data with Lambda .......................................................................................................... 385 Understanding the v2 data export schema .................................................................................. 391 Migration from Kinesis v1 to v2 ...................................................................................................... 410 Monitoring costs .......................................................................................................................... 413 Conceptual overview ............................................................................................................................... 413 Billing tag keys and tag values ............................................................................................................. 414 Retrieving project tag values ................................................................................................................ 414 Retrieving site tag values ....................................................................................................................... |
AmazonMonitronUG-003 | AmazonMonitronUG.pdf | 3 | data to a Kinesis stream ....................................................................................... 380 Editing live data export settings ..................................................................................................... 380 Stopping a live data export ............................................................................................................. 381 Viewing data export errors ............................................................................................................... 381 Using server-side encryption for the Kinesis stream .................................................................. 381 Monitoring with Amazon CloudWatch Logs ................................................................................. 382 Storing exported data in Amazon S3 ............................................................................................. 384 Processing data with Lambda .......................................................................................................... 385 Understanding the v2 data export schema .................................................................................. 391 Migration from Kinesis v1 to v2 ...................................................................................................... 410 Monitoring costs .......................................................................................................................... 413 Conceptual overview ............................................................................................................................... 413 Billing tag keys and tag values ............................................................................................................. 414 Retrieving project tag values ................................................................................................................ 414 Retrieving site tag values ....................................................................................................................... 415 Activating billing tags ............................................................................................................................. 416 Viewing cost reports ............................................................................................................................... 418 App settings ................................................................................................................................. 420 Localization settings ................................................................................................................................ 420 Changing localization settings ......................................................................................................... 420 Logging actions with AWS CloudTrail ........................................................................................ 425 Amazon Monitron information in CloudTrail ..................................................................................... 425 Example: Amazon Monitron log file entries ....................................................................................... 427 Successful DeleteProject action ....................................................................................................... 428 Failed DeleteProject action (authorization error) ........................................................................ 429 Failed DeleteProject action (conflict exception error) ................................................................. 430 Security ........................................................................................................................................ 432 Data Protection ........................................................................................................................................ 432 Data at rest .......................................................................................................................................... 434 viii Amazon Monitron User Guide Data in transit ..................................................................................................................................... 434 AWS KMS and Data Encryption ....................................................................................................... 434 Identity and Access Management ........................................................................................................ 435 Audience ............................................................................................................................................... 435 Authenticating with Identities ......................................................................................................... 436 Managing Access Using Policies ...................................................................................................... 439 How Amazon Monitron Works with IAM ....................................................................................... 441 Using service-linked roles ................................................................................................................. 449 Logging and Monitoring ......................................................................................................................... 456 Compliance Validation ............................................................................................................................ 456 Infrastructure Security ............................................................................................................................ 457 Security Best Practices for Amazon Monitron ................................................................................... 458 Troubleshooting ........................................................................................................................... 459 Troubleshooting Issues with Amazon Monitron Sensors ................................................................. 459 If you can't commission your sensors ............................................................................................ 459 If your sensor is offline ..................................................................................................................... 461 If your sensor falls off ....................................................................................................................... 462 ..................................................................................................................................................................... 462 ................................................................................................................................................................ 463 If commissioning the gateway fails ................................................................................................ 464 ................................................................................................................................................................ 464 Available devices ......................................................................................................................... 467 Quotas .......................................................................................................................................... 468 Supported Regions .................................................................................................................................. 468 Quotas ........................................................................................................................................................ 468 Document history ........................................................................................................................ 469 ix Amazon Monitron User Guide Amazon Monitron is no longer open to new customers. Existing customers can continue to use the service as normal. For capabilities similar to Amazon Monitron, see our blog post. x Amazon Monitron User Guide What is Amazon Monitron? Amazon Monitron is a machine-learning based end-to-end condition monitoring system that detects potential failures within equipment. You can use it to implement a predictive maintenance program and reduce lost productivity from unplanned machine downtime. Amazon Monitron includes purpose-built sensors to capture vibration and temperature data, and gateways to automatically transfer data to the AWS Cloud. Amazon Monitron analyzes data for indications of potential equipment failure and notifies you about developing faults so you can resolve them before they become more serious problems. With Amazon Monitron, you can schedule corrective maintenance activities more effectively to limit productivity losses and minimize repair costs that can result from catastrophic failure of your equipment. Amazon Monitron comes with an application in two versions. The mobile application handles system setup, analytics, and notification when tracking equipment conditions. The web application provides all the same functions as the mobile app except setup. Reliability managers can quickly deploy Amazon Monitron to track the machine health of industrial equipment, such as bearings, motors, gearboxes, and pumps, without any development work or specialized training. What is Amazon Monitron? Amazon Monitron devices Amazon Monitron includes two types of devices: a sensor, for collecting data from your equipment, and a gateway, for sending that data to Amazon Monitron. You can purchase both from Amazon.com or Amazon Business. You mount the sensors directly on the machines (or assets) that you want to monitor. You can place up to 20 sensors on an asset. Amazon Monitron devices 1 Amazon Monitron User Guide An Amazon Monitron sensor Each sensor collects data from the asset and sends it through the AWS Cloud to Amazon Monitron using a gateway mounted on the factory wall and plugged into a standard outlet. The Amazon Monitron Starter Kit, which is available at Amazon.com or Amazon Business, contains five sensors and one Wi-Fi gateway. You can add more sensors and gateways as needed. An Amazon Monitron gateway Amazon Monitron devices 2 Amazon Monitron User Guide Amazon Monitron software Amazon Monitron includes a console, which is used by your IT account manager to create a project and add admin users to manage it. This project is the framework for all the Amazon Monitron tasks that the rest of the team performs to monitor your equipment. Until you set up the project, no other equipment monitoring can be done using Amazon Monitron. IT Manager tasks include the following: • Setting up a user directory to provide users for Amazon Monitron • Creating a project to contain all of your team's Amazon Monitron monitoring tasks, such as creating sites, pairing sensors, adding assets, and so on • Adding an admin user to manage the project Except for |
AmazonMonitronUG-004 | AmazonMonitronUG.pdf | 4 | project and add admin users to manage it. This project is the framework for all the Amazon Monitron tasks that the rest of the team performs to monitor your equipment. Until you set up the project, no other equipment monitoring can be done using Amazon Monitron. IT Manager tasks include the following: • Setting up a user directory to provide users for Amazon Monitron • Creating a project to contain all of your team's Amazon Monitron monitoring tasks, such as creating sites, pairing sensors, adding assets, and so on • Adding an admin user to manage the project Except for the initial project set up, your team performs all monitoring tasks using the Amazon Monitron mobile app, which they install on their smartphones, or the web app, which they can use in their browsers. Using the mobile app, reliability managers in your factory can set up sites, manage users, add assets, and install sensors. Using the web app, they can complete the same tasks, except for installing sensors and gateways. Technicians can use the apps to monitor the health of your equipment, and track and document potential failures. The mobile app displays an icon for each asset, so you can see its condition at a glance. Alert icon Alert definition Healthy: The machine is working normally. Alarm: An alarm has been triggered for one of the positions of this asset, indicating that the machine vibration and temperature are out of the normal range at this position. We recommend that you investigate the issue at the earliest opportunity. An equipment failure might occur if the issue isn't addressed. Amazon Monitron software 3 Amazon Monitron User Guide Alert icon Alert definition Warning: A warning has been triggered for one of the positions of this asset, indicatin g that Amazon Monitron has detected early signs of potential failure. Amazon Monitron identifies warning conditions by analyzing equipment vibration and temperature, using a combination of machine learning and ISO vibration standards. Maintenance: Someone has acknowledged the alarm and is looking into the issue. Asset Healthy-offline: Sensor is offline and the last recorded state was Healthy. No new alerts will be generated till the sensor returns online. Position Healthy-offline: Sensor is offline and the last recorded state was Healthy. No new alerts will be generated till the position returns online. Asset Alarm-offline: Sensor is offline and the last recorded state was an Alarm. No new alerts will be generated till the sensor returns online. Position Alarm-offline: Sensor is offline and the last recorded state was an Alarm. No new alerts will be generated till the position returns online. Amazon Monitron software 4 Amazon Monitron User Guide Alert icon Alert definition Asset Warning-offline: Sensor is offline and the last recorded state was a Warning. No new alerts will be generated till the sensor returns online. Position Warning-offline: Sensor is offline and the last recorded state was a Warning. No new alerts will be generated till the position returns online. Asset Maintenance-offline: Sensor is offline and the last recorded state was Maintenance. No new alerts will be generated till the sensor returns online. Position Maintenance-offline: Sensor is offline and the last recorded state was Maintenance. No new alerts will be generated till the position returns online. No sensor: At least one position for the asset doesn't have a sensor paired to it. No sensor To find out more, you can drill down into the data. Amazon Monitron software 5 Amazon Monitron User Guide Sensor reading of a healthy asset. Sensor reading of an unhealthy asset. Amazon Monitron software 6 Amazon Monitron User Guide As Amazon Monitron collects more data, it improves its machine learning (ML) model and learns to make more accurate estimates of potential machine abnormalities. Benefits of Amazon Monitron Amazon Monitron provides the following key benefits: • Works out of the box – Amazon Monitron sensors and gateways are pre-configured to work with Amazon Monitron software. Reliability managers can install these devices using the app and can start monitoring equipment in just a few hours. It's simple to set up and requires little or no development work, knowledge of ML, or integration. • Immediate notifications in the Amazon Monitron app – Amazon Monitron sends users notifications in the app when it detects abnormal machine patterns. Technicians can view, track, and provide feedback on these abnormal machine states in the Amazon Monitron app. • ISO and ML-based analytics – Amazon Monitron automatically detects abnormal machine operating states. To do this, Amazon Monitron analyzes vibration and temperature signals and compares them to International Standards Organization (ISO 20816) standard thresholds and ML-enabled models. • Support for adding ML feedback in the app– Amazon Monitron offers simple workflows for technicians to enter feedback on the accuracy of the alerts in the app. Amazon Monitron learns from that feedback and continues |
AmazonMonitronUG-005 | AmazonMonitronUG.pdf | 5 | in the app when it detects abnormal machine patterns. Technicians can view, track, and provide feedback on these abnormal machine states in the Amazon Monitron app. • ISO and ML-based analytics – Amazon Monitron automatically detects abnormal machine operating states. To do this, Amazon Monitron analyzes vibration and temperature signals and compares them to International Standards Organization (ISO 20816) standard thresholds and ML-enabled models. • Support for adding ML feedback in the app– Amazon Monitron offers simple workflows for technicians to enter feedback on the accuracy of the alerts in the app. Amazon Monitron learns from that feedback and continues to improves over time. Pricing for Amazon Monitron Amazon Monitron includes both one-time, device purchase costs for the sensors and gateways, and an ongoing pay-as-you-go service fee per Amazon Monitron sensor in use. There are no additional upfront fees and no long-term commitments. For information, see Amazon Monitron Pricing. Related resources The following documentation and other resources are available for Amazon Monitron: • Amazon Monitron Getting Started Guide – For IT managers, reliability managers, and technicians, this guide gets you started using Amazon Monitron. It shows you how to set up Amazon Monitron, create assets, set up sensors, and start monitoring your equipment. Benefits of Amazon Monitron 7 Amazon Monitron User Guide • Amazon Monitron User Guide – This detailed guide provides reliability managers (admin users) and technicians with more in-depth information about using Amazon Monitron to monitor your equipment for machine abnormalities. It also describes how to use the app, your primary Amazon Monitron tool. Are you a first-time user of Amazon Monitron? How you interact with Amazon Monitron depends on your role as an Amazon Monitron user. Select the role that fits you best from the options below to see a recommended set of topics to help you learn more about Amazon Monitron. IT Manager An IT manager sets up an Amazon Monitron project, configures a user directory to add Amazon Monitron users, adds site admin users to manager projects, and can also check Amazon Monitron logs in AWS CloudTrail. If you are a first-time IT Manager user of Amazon Monitron, we recommend that you read the following sections in order: 1 2 3 4 5 6 7 How Setting up Projects Managing Understan Accessing Security Amazon a project Monitron works Explains how to manage Amazon Monitron projects Introduce s Amazon Monitron component s and describes how Explains how to setup the AWS console for creating Amazon Amazon Monitron projects admin users Explains how to add and remove admin users to and from your Amazon ding your data networkin g with Amazon Monitron Explains Amazon Monitron hardware networkin g Explains how to export your Amazon Monitron data with Explains how to configure Amazon Monitron to meet your Kinesis or security download and Are you a first-time user of Amazon Monitron? 8 Amazon Monitron User Guide 1 2 3 4 5 6 7 How Setting up Projects Managing Understan Accessing Security Amazon a project Monitron works Monitron works admin users Monitron projects ding your data networkin g with Amazon Monitron it to Amazon S3 complianc e objective s Reliability manager/Admin user A reliability manager/admin user has full access to all resources within an Amazon Monitron project or site. As a reliability manager or site admin user, you can add other users, create assets, pair sensors to assets, monitor assets, acknowledge alerts, and resolve abnormalities. If you are a first-time reliability manager or admin user of Amazon Monitron, we recommend that you read the following sections in order: 1 2 3 4 5 6 7 How Adding Sites Ethernet Wi-Fi Assets Managing gateways gateways users Describes how to create and manage sites Explains how to set up and configure ethernet gateways Explains how to set up and configure Wi-Fi gateways Describes how to manage assets and sensors Describes how to manage admin users Amazon assets and Monitron installin works g devices Introduce s Amazon Monitron component s and describes how Explains how to install Amazon Monitron gateways, add assets, Amazon and attach sensors Are you a first-time user of Amazon Monitron? 9 Amazon Monitron User Guide 1 2 3 4 5 6 7 How Adding Sites Ethernet Wi-Fi Assets Managing gateways gateways users Amazon assets and Monitron installin works g devices Monitron works Technician A technician user has read-only permissions to a Amazon Monitron project or site to which they have been added. Technicians also have permissions for monitoring assets and acknowledging and resolving abnormalities. If you are a first-time technician user of Amazon Monitron, we recommend that you read the following sections in order: 1 2 3 4 5 6 How Amazon Assets Understan Ethernet Wi-Fi Troublesh ding sensor gateways gateways ooting Amazon Monitron device issues Monitron works Describes how to manage assets and sensors Introduce s Amazon Monitron |
AmazonMonitronUG-006 | AmazonMonitronUG.pdf | 6 | gateways gateways users Amazon assets and Monitron installin works g devices Monitron works Technician A technician user has read-only permissions to a Amazon Monitron project or site to which they have been added. Technicians also have permissions for monitoring assets and acknowledging and resolving abnormalities. If you are a first-time technician user of Amazon Monitron, we recommend that you read the following sections in order: 1 2 3 4 5 6 How Amazon Assets Understan Ethernet Wi-Fi Troublesh ding sensor gateways gateways ooting Amazon Monitron device issues Monitron works Describes how to manage assets and sensors Introduce s Amazon Monitron components and describes how Amazon Monitron works measureme nts and monitorin g machine abnormali ties Explains how to understan d sensor measureme nts and monitor machine Explains how to set up and configure ethernet gateways Explains how to set up and configure Wi- Fi gateways Explains how to troublesh oot Amazon Monitron device issues Are you a first-time user of Amazon Monitron? 10 Amazon Monitron User Guide 1 2 3 4 5 6 How Amazon Assets Understan Ethernet Wi-Fi Troublesh Monitron works ooting Amazon Monitron device issues ding sensor gateways gateways measureme nts and monitorin g machine abnormali ties abnormali ties Are you a first-time user of Amazon Monitron? 11 Amazon Monitron User Guide How Amazon Monitron works Amazon Monitron is a machine learning end-to-end condition monitoring solution system that detects developing faults within machinery, enabling you to implement a predictive maintenance program and reduce lost productivity from unplanned machine downtime. Amazon Monitron includes purpose-built sensors to capture vibration and temperature data, gateways to automatically transfer data to the AWS Cloud, and an application for system set up, analytics, and notification when tracking equipment condition. Amazon Monitron sensors use an ISO threshold model and a machine learning (ML) model to monitor vibration. The ISO model is used to analyze the magnitude of vibration (machine condition). The ML model is used to detect change in vibration (change in machine condition). Reliability managers can deploy Amazon Monitron to track machine health of industrial equipment, such as bearings, motors, gearboxes, and pumps, without any development work or specialized training. Tip Check your Amazon Monitron app regularly for updates and access to the latest features. Topics • The Amazon Monitron workflow • Amazon Monitron concepts • Amazon Monitron components • Amazon Monitron alerts The Amazon Monitron workflow The following diagram shows the basic workflow of Amazon Monitron. Amazon Monitron workflow 12 Amazon Monitron User Guide 1. An Amazon Monitron sensor captures temperature and vibration data from the equipment (the asset) and transmits it to the gateway. 2. An Amazon Monitron gateway transmits the data to the AWS Cloud using the factory's internet connection. 3. The Amazon Monitron ML-based service in the AWS Cloud analyzes the sensor data. a. Amazon Monitron looks for abnormalities in the data that could indicate developing faults. b. If Amazon Monitron finds potential failures, it notifies reliability managers and technicians through the Amazon Monitron app so they can take appropriate action. c. Technicians investigate based on the alerts, and resolve the developing fault. They enter feedback on the accuracy of the alerts, and report the failure mode, cause, and action taken in the app. Amazon Monitron learns from this feedback and continually improves. 4. The app displays current and past temperature and vibration data in charts that are easy to understand and can be used while investigating an issue. Amazon Monitron concepts An Amazon Monitron implementation is structured in the following way: PROJECT → SITE → ASSET → SENSOR → POSITION The following table explains the Amazon Monitron concepts and terminology you need to know to get started with Amazon Monitron: Amazon Monitron concepts 13 Amazon Monitron User Guide Concept name Concept definition Key facts Common users Project • Where you set up the gateways, • Resources can't be shared between • IT administrators/ managers assets, and sensors projects used by Amazon Monitron • Can only be created on the • Captures details of Amazon Monitron Amazon Monitron console detected machine abnormalities that can lead to equipment failure • Can only be created and managed by IT managers or users with access to the Amazon Monitron console Site • A collection of • Helpful for • IT administrators/ assets, gateways, organization if managers and sensors that your project has share a purpose a large pool of • Used to organize projects to make assets, gateways, and sensors them easier to • Can be used to • Reliability managers manage control access and permissions • Can create up to 50 sites within a project and add up to 100 assets and 200 gateways to each site • Must be a project- level admin user Amazon Monitron concepts 14 Amazon Monitron User Guide Concept name Concept definition Key facts Common users to |
AmazonMonitronUG-007 | AmazonMonitronUG.pdf | 7 | collection of • Helpful for • IT administrators/ assets, gateways, organization if managers and sensors that your project has share a purpose a large pool of • Used to organize projects to make assets, gateways, and sensors them easier to • Can be used to • Reliability managers manage control access and permissions • Can create up to 50 sites within a project and add up to 100 assets and 200 gateways to each site • Must be a project- level admin user Amazon Monitron concepts 14 Amazon Monitron User Guide Concept name Concept definition Key facts Common users to add a site to a project • Can be configure d using both the mobile and web app Gateway • Wi-Fi or Ethernet devices that • Helpful for keeping track of whether • Reliability managers transfer the data sensor data is • Technicians collected by Amazon Monitron senors to the AWS Cloud. being correctly transferred to the Cloud. • Must be commissio ned using the mobile app Asset • The pieces of • Basis for viewing equipment on your the health of your factory floor machines • Can be: • Amazon Monitron • individual machines • sections of a large piece of equipment • part of an industrial process • any element of your manufactu ring model sensors are paired to assets and their parts • Can place sensors on up to 20 positions on an asset • Can be configured using both mobile and web app • Reliability managers • Technicians Amazon Monitron concepts 15 Amazon Monitron User Guide Concept name Concept definition Key facts Common users Sensor • Collects temperatu re and vibration on up to 20 • Can place sensors • Technicians • Reliability managers data from your positions on each equipment asset • Amazon Monitron • Can be assigned uses the data to a machine class detect developing corresponding to issues the machine part it's placed on • Can be configured using mobile app only Amazon Monitron concepts 16 Amazon Monitron User Guide Concept name Concept definition Key facts Common users Position • The place on the asset where you on up to 20 • Can place sensors • Technicians • Reliability managers mount a sensor positions on each • Important for collecting and analyzing data asset • Positions on the same asset can be assigned different machine classes for a fine-grained view of machine health Important If you have complex machinery with more than one potential point of failure, we recommend that you collect data from multiple positions. Amazon Monitron components Amazon Monitron includes purpose-built sensors to capture vibration and temperature data, as well as gateways to automatically transfer data to the AWS Cloud. It also comes with an application in two versions. The mobile application handles system setup, analytics, and notification when Amazon Monitron components 17 Amazon Monitron User Guide tracking equipment conditions. The web application provides all the same functions as the mobile app except setup. The Amazon Monitron Starter Kit, which is available at Amazon.com or Amazon Business, contains five sensors and one Wi-Fi gateway. You can buy and add more sensors and gateways as needed. For more information, see Amazon Monitron FAQs. The following table shows Amazon Monitron components, their functions, and their use cases. Note Vibration is measured in milimeters (mm) and inches. Temperature is measured in Fahrenheit (F) and Celcius (C). Component name Component details Function Common users Sensor • Technicians • Reliability managers • Hardware • Temperature: -20C – +80C/ -4F – +176F • Dimension s: 52.8x43.0 x24.9mm/2 .08x1.69x0.98 inch • Weight: 54 gms • IP Rating: IP65 • Wireless protocol: Bluetooth Low Energy 5 • Vibration sensor: 3-axis MEMs accelerometer, range +/-16g, frequency response up to 6kHz, • Captures vibration and temperature data directly from machines (assets) • Sends collected data to the AWS Cloud using either Wi-Fi or Ethernet gateways • Up to 20 can be placed on a machine (asset) • Each sensor can be assigned a machine class corresponding to the machine (asset) part it's placed on Amazon Monitron components 18 Amazon Monitron User Guide Component name Component details Function Common users sampling frequency 26.7kHz • Power: Lithium metal non-recha rgeable batteries • Battery life: Estimated 5 years • Default data capture: once an hour Ethernet gateway • Hardware • Sends vibration • Technicians • Temperature: -20C – +60C/ -4F – +140F and temperature data collected from machines (assets) • Reliability managers • Dimension s: 13.9X10.7 X4.1cm/5. 5X4.2X1.6 inch • Weight: 230 gms/8.20 oz • IP Rating: IP65 • Internet connectiv ity: RJ45 10/100Mbps • Power: IEEE 802.3at type1 (15.4 Watt class) to AWS Cloud • Powered by a Ethernet Cat 5e or Cat 6 cord plugged into its RJ-45 socket • Doesn't need to be directly attached to asset (machine) • Needs a |
AmazonMonitronUG-008 | AmazonMonitronUG.pdf | 8 | life: Estimated 5 years • Default data capture: once an hour Ethernet gateway • Hardware • Sends vibration • Technicians • Temperature: -20C – +60C/ -4F – +140F and temperature data collected from machines (assets) • Reliability managers • Dimension s: 13.9X10.7 X4.1cm/5. 5X4.2X1.6 inch • Weight: 230 gms/8.20 oz • IP Rating: IP65 • Internet connectiv ity: RJ45 10/100Mbps • Power: IEEE 802.3at type1 (15.4 Watt class) to AWS Cloud • Powered by a Ethernet Cat 5e or Cat 6 cord plugged into its RJ-45 socket • Doesn't need to be directly attached to asset (machine) • Needs a Power over Ethernet (POE) supported router or a POE power injector to work Amazon Monitron components 19 Amazon Monitron User Guide Component name Component details Function Common users Wi-Fi gateway • Hardware • Sends vibration • Technicians • Temperature: 0C – 40C/ 32F – 104F • Dimension s: 90x78x38 mm/3.6x3.1x1.5 inch • Weight: 95gms • IP Rating: IP65 • Internet connectiv ity: WiFi, 802.11b/ g/n, ISM 2.4GHz only • Power: 5.0V–2.0 DC, AC adapter included for USA, UK, and EU countries (indoors only) • Reliability managers and temperature data collected from machines (assets) to AWS Cloud • Wi-Fi (plugged into a standard socket) Note Wi-Fi gateways aren't supported in all sites. Amazon Monitron ethernet gateways are the global standard. Amazon Monitron components 20 Amazon Monitron User Guide Component name Component details Function Common users Console Software • Signing up for AWS • IT managers • Creating an • IT administrators Amazon Monitron project • Reliability managers • Creating and initially assigning admin users to manage projects Important Must be set- up first for Amazon Monitron to work. Mobile app Software • Managing an • Technicians Amazon Monitron project • Reliability managers • (Project-level admin user only) Creating sites • Creating assets • Monitoring equipment condition • (Mobile app only) Setting up sensors and gateways Amazon Monitron components 21 Amazon Monitron User Guide Component name Component details Function Common users Web app Software • Managing an • Technicians Amazon Monitron project • Reliability managers • (Project-level admin user only) Creating sites • Creating assets • Monitoring equipment condition Important The web app supports all tasks supported by the mobile app except installing sensors and gateways. Amazon Monitron alerts To track equipment health, the Amazon Monitron mobile app displays an icon for each asset, so you can see its condition at a glance. The following table shows the status icons you may see for your asset. Amazon Monitron alerts 22 Amazon Monitron User Guide Alert icon Alert definition Healthy: The machine is working normally. Alarm: An alarm has been triggered for one of the positions of this asset, indicating that the machine vibration and temperature are out of the normal range at this position. We recommend that you investigate the issue at the earliest opportunity. An equipment failure might occur if the issue isn't addressed. Warning: A warning has been triggered for one of the positions of this asset, indicatin g that Amazon Monitron has detected early signs of potential failure. Amazon Monitron identifies warning conditions by analyzing equipment vibration and temperature, using a combination of machine learning and ISO vibration standards. Maintenance: Someone has acknowledged the alarm and is looking into the issue. Asset Healthy-offline: Sensor is offline and the last recorded state was Healthy. No new alerts will be generated till the sensor returns online. Position Healthy-offline: Sensor is offline and the last recorded state was Healthy. No new alerts will be generated till the position returns online. Amazon Monitron alerts 23 Amazon Monitron User Guide Alert icon Alert definition Asset Alarm-offline: Sensor is offline and the last recorded state was an Alarm. No new alerts will be generated till the sensor returns online. Position Alarm-offline: Sensor is offline and the last recorded state was an Alarm. No new alerts will be generated till the position returns online. Asset Warning-offline: Sensor is offline and the last recorded state was a Warning. No new alerts will be generated till the sensor returns online. Position Warning-offline: Sensor is offline and the last recorded state was a Warning. No new alerts will be generated till the position returns online. Asset Maintenance-offline: Sensor is offline and the last recorded state was Maintenance. No new alerts will be generated till the sensor returns online. Position Maintenance-offline: Sensor is offline and the last recorded state was Maintenance. No new alerts will be generated till the position returns online. Amazon Monitron alerts 24 Amazon Monitron User Guide Getting started This chapter explains the basic steps to get started with Amazon Monitron: 1. Setting up a project—This provides the framework for the rest of your team to monitor your equipment. It uses the Amazon Monitron console and will probably only need to be done occasionally, or even just |
AmazonMonitronUG-009 | AmazonMonitronUG.pdf | 9 | offline and the last recorded state was Maintenance. No new alerts will be generated till the sensor returns online. Position Maintenance-offline: Sensor is offline and the last recorded state was Maintenance. No new alerts will be generated till the position returns online. Amazon Monitron alerts 24 Amazon Monitron User Guide Getting started This chapter explains the basic steps to get started with Amazon Monitron: 1. Setting up a project—This provides the framework for the rest of your team to monitor your equipment. It uses the Amazon Monitron console and will probably only need to be done occasionally, or even just once, depending on the number of projects you choose to have. All other tasks are done through the Amazon Monitron mobile app. 2. Adding assets and installing devices—All of these tasks are done using the mobile app. It's a major activity at the beginning of the project. You can add a few assets and install just a few devices at first, and then come back to it with additional assets later on. 3. Understanding alerts—This is the daily use of Amazon Monitron and is done using the mobile app. It consists of daily monitoring, as well as the tasks that have to be dealt with when Amazon Monitron discovers a possible machine abnormality. To learn more about Amazon Monitron, you can visit the Amazon Monitron product detail page. Topics • Setting up a project • Adding assets and installing devices • Understanding warnings and alerts Setting up a project The first step with Amazon Monitron is to set up your project in the Amazon Monitron console. A project is where your team sets up gateways, assets, and sensors in the Amazon Monitron mobile app. Topics • Step 1: Create an account • Step 2: Create a project • Step 3: Create admin users • Step 4: (optional) Add Amazon Monitron users to your project • Step 5: Invite users to your project Setting up a project 25 Amazon Monitron User Guide Step 1: Create an account Sign up for an AWS account If you do not have an AWS account, complete the following steps to create one. To sign up for an AWS account 1. Open https://portal.aws.amazon.com/billing/signup. 2. Follow the online instructions. Part of the sign-up procedure involves receiving a phone call and entering a verification code on the phone keypad. When you sign up for an AWS account, an AWS account root user is created. The root user has access to all AWS services and resources in the account. As a security best practice, assign administrative access to a user, and use only the root user to perform tasks that require root user access. AWS sends you a confirmation email after the sign-up process is complete. At any time, you can view your current account activity and manage your account by going to https://aws.amazon.com/ and choosing My Account. Create a user with administrative access After you sign up for an AWS account, secure your AWS account root user, enable AWS IAM Identity Center, and create an administrative user so that you don't use the root user for everyday tasks. Secure your AWS account root user 1. Sign in to the AWS Management Console as the account owner by choosing Root user and entering your AWS account email address. On the next page, enter your password. For help signing in by using root user, see Signing in as the root user in the AWS Sign-In User Guide. 2. Turn on multi-factor authentication (MFA) for your root user. For instructions, see Enable a virtual MFA device for your AWS account root user (console) in the IAM User Guide. Step 1: Create an account 26 Amazon Monitron User Guide Create a user with administrative access 1. Enable IAM Identity Center. For instructions, see Enabling AWS IAM Identity Center in the AWS IAM Identity Center User Guide. 2. In IAM Identity Center, grant administrative access to a user. For a tutorial about using the IAM Identity Center directory as your identity source, see Configure user access with the default IAM Identity Center directory in the AWS IAM Identity Center User Guide. Sign in as the user with administrative access • To sign in with your IAM Identity Center user, use the sign-in URL that was sent to your email address when you created the IAM Identity Center user. For help signing in using an IAM Identity Center user, see Signing in to the AWS access portal in the AWS Sign-In User Guide. Assign access to additional users 1. In IAM Identity Center, create a permission set that follows the best practice of applying least- privilege permissions. For instructions, see Create a permission set in the AWS IAM Identity Center User Guide. 2. Assign users to a group, and then assign single sign-on access |
AmazonMonitronUG-010 | AmazonMonitronUG.pdf | 10 | with your IAM Identity Center user, use the sign-in URL that was sent to your email address when you created the IAM Identity Center user. For help signing in using an IAM Identity Center user, see Signing in to the AWS access portal in the AWS Sign-In User Guide. Assign access to additional users 1. In IAM Identity Center, create a permission set that follows the best practice of applying least- privilege permissions. For instructions, see Create a permission set in the AWS IAM Identity Center User Guide. 2. Assign users to a group, and then assign single sign-on access to the group. For instructions, see Add groups in the AWS IAM Identity Center User Guide. Important Amazon Monitron supports all IAM Identity Center regions except opt-in and government regions. For a list of supported regions, see Understanding SSO requirements. Step 1: Create an account 27 Amazon Monitron Step 2: Create a project User Guide Now that you've signed in to the AWS Management Console, you can use the Amazon Monitron console to create your project. To create a project 1. Choose the AWS Region that you want to use in the Region selector. Amazon Monitron is available only in the US East (N. Virginia), Europe (Ireland), and Asia Pacific (Sydney) Regions. 2. Open the Amazon Monitron console at https://console.aws.amazon.com/monitron. 3. Choose Create project. 4. Under Project Details, for Project name, enter a name for the project. 5. (Optional) Under Data encryption, you can check Custom encryption settings (advanced) if you have an AWS KMS key in AWS Key Management Service. Amazon Monitron encrypts all data at rest and in transit. If you don't provide your own CMK, your data is encrypted by a CMK that Amazon Monitron owns and manages. For more information about encryption for your project, see KMS and Data Encryption in Amazon Monitron. 6. (Optional) To add a tag to the project, enter a key-value pair under Tags and then choose Add tag. For more information about tags, see Tags in Amazon Monitron. 7. Choose Next to create the project. Step 2: Create a project 28 Amazon Monitron User Guide When you create your first project, the owner of the AWS account will get an email from AWS Organizations. No action needs to be taken based on this email. Step 3: Create admin users Give access to one or more people in your organization (such as reliability managers) as admin users. An admin user is a person who belongs to an Amazon Monitron project and who can add other users to the project. When you add an admin user, Amazon Monitron creates an account for that user in AWS IAM Identity Center. IAM Identity Center is a service that helps you manage SSO access to AWS accounts and applications in your organization. Amazon Monitron uses IAM Identity Center to authenticate users for the Amazon Monitron mobile app. If you haven't enabled IAM Identity Center in your AWS account, Amazon Monitron enables it for you when you create your first Amazon Monitron admin user. If you are already using IAM Identity Center in your account, then your IAM Identity Center users are shown in the Amazon Monitron console. Complete the steps in this section to add yourself to your project as an admin user. Repeat them for each additional admin user that you want to create. To create an admin user Unless you already use IAM Identity Center in your AWS account, use Amazon Monitron to create admin users. If these users are already in IAM Identity Center, you can skip creating the users, and you are ready to assign the admin role to them. 1. Open the Amazon Monitron console at https://console.aws.amazon.com/monitron. 2. On the Add project admin user page, choose Create user. 3. In the Create user section, enter the admin user's email address and name. 4. Choose Create user. Amazon Monitron creates a user in IAM Identity Center. IAM Identity Center sends the user an email that contains a link to activate the account. The link is valid for up to seven days. Within this time, each user must open the email and accept the invitation. Step 3: Create admin users 29 Amazon Monitron User Guide To assign the admin role to the admin users 1. On the Add project admin user page, select the checkbox for each admin user that you created. 2. Choose Add. You can add admin users to your project even if those people have not yet accepted the invitations to their IAM Identity Center accounts. Step 4: (optional) Add Amazon Monitron users to your project In addition to admin users, you can also add users who lack admin permissions. For example, these users might be technicians who only use the Amazon Monitron mobile app to monitor assets, acknowledge notifications |
AmazonMonitronUG-011 | AmazonMonitronUG.pdf | 11 | User Guide To assign the admin role to the admin users 1. On the Add project admin user page, select the checkbox for each admin user that you created. 2. Choose Add. You can add admin users to your project even if those people have not yet accepted the invitations to their IAM Identity Center accounts. Step 4: (optional) Add Amazon Monitron users to your project In addition to admin users, you can also add users who lack admin permissions. For example, these users might be technicians who only use the Amazon Monitron mobile app to monitor assets, acknowledge notifications and enter closure codes. For users who are not admin users: • You use IAM Identity Center, not Amazon Monitron, to create their user accounts. • You use the Amazon Monitron mobile app to add the users to projects, not the Amazon Monitron console. Topics • To add users to IAM Identity Center • To add a user using the mobile app • How to add a user using the web app To add users to IAM Identity Center If your users already have accounts in IAM Identity Center in your AWS account, you can skip these steps. You are ready to add the users to your project in the mobile app. Otherwise, add your users to IAM Identity Center by completing the following steps. Note The following steps are not required if all of your users are admin users. Step 4: (optional) Add Amazon Monitron users to your project 30 Amazon Monitron User Guide 1. Open the AWS IAM Identity Center console at https://console.aws.amazon.com/singlesignon/. 2. In the IAM Identity Center console, choose Users. 3. Repeat the following steps for each user that will access your project in the Amazon Monitron mobile app. a. On the Users page choose Add user. b. In the User details section, provide the username and contact information. Leave Password set to Send an email to the user with password setup instructions. c. Choose Next: Groups. d. Choose Add user. IAM Identity Center sends the user an email that contains a link to activate the IAM Identity Center user. The link is valid for up to seven days. Each user must open the email and accept the invitation before accessing your project in the Amazon Monitron mobile app. To add a user using the mobile app 1. Log into the Amazon Monitron mobile app on your smartphone. 2. Navigate to the project or site that you want to add a user to, and then to the Users list. 3. Choose Add user. Step 4: (optional) Add Amazon Monitron users to your project 31 Amazon Monitron User Guide 4. Enter a user name. Amazon Monitron searches the user directory for the user. 5. Choose the user from the list. 6. Choose the role that you want to assign the user: Admin, Technician, or Viewer. 7. Choose Add. The new user appears on the Users list. 8. Send the new user an email invitation with a link for accessing the project and downloading the Amazon Monitron mobile app. For more information, see Sending an email invitation. How to add a user using the web app 1. Select Users from the navigation pane. 2. Choose Add user. Step 4: (optional) Add Amazon Monitron users to your project 32 Amazon Monitron User Guide 3. Enter a user name. Amazon Monitron searches the user directory for the user. 4. Choose the user from the list. 5. Choose the role that you want to assign the user: Admin, Technician, or Read only. 6. Choose Add. The new user appears on the Users list. 7. Send the new user an email invitation with a link for accessing the project and downloading the Amazon Monitron mobile app. For more information, see Sending an email invitation. Step 4: (optional) Add Amazon Monitron users to your project 33 Amazon Monitron User Guide Step 5: Invite users to your project Invite the users you've added to your Amazon Monitron project. 1. Open the Amazon Monitron console at https://console.aws.amazon.com/monitron. 2. In the navigation pane, choose Projects. 3. On the Projects page, choose your project name to open its details page. 4. Repeat the following steps for each user that you want to invite. a. Under How it works, choose Email instructions. Step 5: Invite users to your project 34 Amazon Monitron User Guide Your email client opens a draft that contains an invitation to your Amazon Monitron project. It contains both a link to download the Amazon Monitron mobile app from the Google Play Store and a link to open the project. b. Email this message to the user. Adding assets and installing devices Once you've created a project, you or reliability managers and technicians from your team can use the Amazon Monitron mobile app to add |
AmazonMonitronUG-012 | AmazonMonitronUG.pdf | 12 | user that you want to invite. a. Under How it works, choose Email instructions. Step 5: Invite users to your project 34 Amazon Monitron User Guide Your email client opens a draft that contains an invitation to your Amazon Monitron project. It contains both a link to download the Amazon Monitron mobile app from the Google Play Store and a link to open the project. b. Email this message to the user. Adding assets and installing devices Once you've created a project, you or reliability managers and technicians from your team can use the Amazon Monitron mobile app to add gateways, create assets and pair sensors to them, and start monitoring your equipment. Only smartphones using Android 8.0+ or iOS 14+ with Near Field Communication (NFC) and Bluetooth are supported by Amazon Monitron. Your IT manager or reliability manager will generate an email describing how to log in for the first time and connect to your project and send this to you. Once you've logged in for the first time, you can follow the steps to add gateways and install devices. Topics • Step 1: Add a Gateway • Step 2: Adding Assets • Step 3: Attach Sensors • Step 4: Pairing Sensors to an Asset Step 1: Add a Gateway In Amazon Monitron, sensors collect data from machines and pass it to gateways, which transmit the data to the AWS Cloud and thus to Amazon Monitron for analysis. These gateways are usually mounted on the wall of a factory within 20 to 30 meters from the sensor and connect to the AWS Cloud using the local Wi-Fi network. Before adding a gateway, make sure that Bluetooth is turned on for your smartphone. You can only add gateways using the mobile app. Topics • To add a Wi-Fi gateway • To add an Ethernet gateway Adding assets and installing devices 35 Amazon Monitron To add a Wi-Fi gateway User Guide 1. Choose the menu icon (☰), and then choose Getting Started. 2. Choose Add gateway. 3. In your factory, position your gateway in the location that works best for communicating with your sensors. The best place to mount your gateway is higher than the sensors and no more than 20 to 30 meters away. For more information about locating gateways, see Where to Install Your Gateway in the Amazon Monitron User Guide. 4. Plug the gateway in and make sure that the LED lights on the top alternatively blink yellow and blue. Step 1: Add a Gateway 36 Amazon Monitron User Guide 5. Push the button on the side of the gateway to put it into commissioning mode. The lights will start blinking rapidly. 6. In the mobile app, choose Next. 7. Choose Add gateway. Amazon Monitron searches for the gateway, which can take a few moments. When it finds it, the gateway appears in the gateway list. If it can't find the gateway, see Setting Up Gateways in the Amazon Monitron User Guide for possible solutions. 8. When you see the new gateway in the list, choose it. It can take a few moments for Amazon Monitron to connect to the new gateway. 9. After it connects to the gateway, Amazon Monitron scans for Wi-Fi networks. Choose the Wi-Fi network that you want to use. Step 1: Add a Gateway 37 Amazon Monitron Note User Guide When the gateway is successfully connected, Amazon Monitron displays the gateway device ID and MAC ID in the mobile app. 10. Enter your Wi-Fi password, and then choose Connect. It can take a few minutes for the gateway to be commissioned. To add an Ethernet gateway 1. If Bluetooth isn't already turned on for your smartphone, turn it on. 2. Position your gateway in the location that works best for communicating with your sensors. The best place to mount your gateway is higher than the sensors and no more than 20 to 30 meters away.For additional help with locating your gateway, see Placing and installing an Ethernet gateway. 3. Plug in the gateway and make sure the network light (yellow) and the Bluetooth light (blue) on the front of your gateway are blinking alternatively. 4. Push the Config button on the gateway to put it into commissioning mode. the Bluetooth and network LED lights will start flashing rapidly. 5. Open the mobile app on your smartphone. 6. On the Getting started page or the Gateways page, choose Add gateway. Amazon Monitron scans for the gateway. This can take a few moments. when Amazon Monitron finds the gateway, it displays it in the gateway list. 7. Choose the gateway. It can take a few moments for Amazon Monitron to connect to the new gateway. Step 1: Add a Gateway 38 Amazon Monitron User Guide If the mobile app continues to try to connect to |
AmazonMonitronUG-013 | AmazonMonitronUG.pdf | 13 | put it into commissioning mode. the Bluetooth and network LED lights will start flashing rapidly. 5. Open the mobile app on your smartphone. 6. On the Getting started page or the Gateways page, choose Add gateway. Amazon Monitron scans for the gateway. This can take a few moments. when Amazon Monitron finds the gateway, it displays it in the gateway list. 7. Choose the gateway. It can take a few moments for Amazon Monitron to connect to the new gateway. Step 1: Add a Gateway 38 Amazon Monitron User Guide If the mobile app continues to try to connect to the gateway without success, see Troubleshooting Ethernet gateway detection. Note When the gateway is successfully connected, Amazon Monitron displays the gateway device ID and MAC ID in the mobile app. 8. After it connects to the gateway, Amazon Monitron will provide two options for you to configure the network connection for your gateway. 9. Choose your network configuation. It can take a few minutes for the gateway to be commissioned and to connect to the network. Step 1: Add a Gateway 39 Amazon Monitron User Guide If you have further difficulties making the gateway work, it might be helpful to reset it. For more information, see ???. a. If you choose automatic (DHCP), Amazon Monitron will automatically configure the network to connect the gateway. b. If you choose manual, enter your IP address, subnet mask, router, preferred DNS server, and alternate DNS server (optional) information. then choose connect. Step 2: Adding Assets In Amazon Monitron, the machines you monitor are known as assets. Assets are usually individual machines, but they can also be specific sections of equipment. Assets are paired to sensors, which directly monitor temperature and vibration to check for potential failures. You can add assets using both the Amazon Monitron web app and the Amazon Monitron mobile app. Step 2: Adding Assets 40 User Guide Amazon Monitron Topics • Adding assets using the mobile app • Adding assets using the web app Adding assets using the mobile app To add an asset using the mobile app 1. Sign in to your mobile app and select the project you want to add an asset to. Step 2: Adding Assets 41 Amazon Monitron User Guide Step 2: Adding Assets 42 Amazon Monitron User Guide 2. Make sure you're on the correct site your project that you want to add the asset to. The project or site name indicates that you are at that level in the app. For more information about changing from site level to project level and vice versa, see Navigating between projects and sites in the mobile app. 3. From the Assets page, choose Add asset. 4. On the Add asset page, for Asset name, add a name for the asset you want to create and then select Add. Step 2: Adding Assets 43 Amazon Monitron User Guide Step 2: Adding Assets 44 Amazon Monitron Note User Guide If you have a QR code identifying the asset name, you can scan it by selecting the QR code. When you've added your first asset, it's displayed on the Assets list page. Adding assets using the web app To add an asset using the web app 1. Sign in to your web app and select the project you want to add an asset to. 2. From the left navigation menu, choose Sites, and then select the site you want to the asset to. Step 2: Adding Assets 45 Amazon Monitron Note You can also add the asset directly to a project. 3. From the Assets page, choose Add asset. User Guide 4. On the Add asset page, for Asset name, add a name for the asset you want to create and then select Add asset. When you've added your first asset, it's displayed on the Assets list page. Step 2: Adding Assets 46 Amazon Monitron Step 3: Attach Sensors User Guide Assets are paired to sensors, which directly monitor an asset's health. You place each sensor on the asset in a position that you want to monitor. You can place one or more sensors on each asset. Each sensor takes vibration and temperature measurements at the position to which it is paired and sends it to the AWS Cloud for analysis of machine health using the gateway. Where to Place Sensors When placing a sensor, choose a location where it can accurately detect the machine's temperature or vibration. To achieve the greatest accuracy: • Mount the sensor directly onto the housing of the target component. • Minimize the length of the vibration transmission path, the distance between the source of vibration and sensor. • Avoid mounting the sensor in a location that can oscillate due to natural frequencies, such as sheet metal covers. Vibration will attenuate up to 30-36"/75-90 |
AmazonMonitronUG-014 | AmazonMonitronUG.pdf | 14 | it is paired and sends it to the AWS Cloud for analysis of machine health using the gateway. Where to Place Sensors When placing a sensor, choose a location where it can accurately detect the machine's temperature or vibration. To achieve the greatest accuracy: • Mount the sensor directly onto the housing of the target component. • Minimize the length of the vibration transmission path, the distance between the source of vibration and sensor. • Avoid mounting the sensor in a location that can oscillate due to natural frequencies, such as sheet metal covers. Vibration will attenuate up to 30-36"/75-90 cm) from the source. Attributes of the vibration transmission path length that can reduce the transmission path length include: • The number of mounting surfaces, causing signal reflection • Materials such as rubber and plastic that can absorb vibration The following examples show where to place sensors. For more information and examples, see Where to Place Your Sensors in the Amazon Monitron User Guide. Step 3: Attach Sensors 47 Amazon Monitron User Guide How to Place Sensors When you've decided where to place a sensor on an asset, make sure that a minimum of one- third of the sensor base is fixed to the asset. The sensors can pick up vibration and temperature measurements across the entire base of the sensor, but it's important to have the asset target area centered as much as possible on the sensor as shown in the following image. Step 3: Attach Sensors 48 Amazon Monitron User Guide Attach the sensor with an industrial adhesive. We recommend a cyanoacrylate-type epoxy. For additional information about attaching the sensor to your asset, see How to Place the Sensors in the Amazon Monitron User's Guide. Warning Amazon Monitron sensors can be attached to the equipment using industrial adhesive. We suggest you check the surface before selecting the adhesive. For surfaces up to 5 mm roughness/gaps, you can select an adhesive that fills the gap, such as LOCTITE® 3090 or LOCTITE® 4070. For flat surfaces (<0.1mm roughness), you can select a more generic adhesive, such as LOCTITE® 454. Always check and follow the processing guidelines outlined by the adhesive vendor. For more information about safely using the adhesive, see Loctite 454 Technical Information, Loctite 3090 Technical Information, or Loctite 4070 Technical Information, as appropriate. To attach the Amazon Monitron sensor 1. Apply a thin layer of the adhesive on the bottom of the sensor, maximizing the contact area. 2. Hold the sensor to the mounting location on the machine part, pressing firmly for the length of time specified by the adhesive instructions. Step 4: Pairing Sensors to an Asset Each sensor that you pair to an asset has a designated position and is set to monitor a specific part of the asset. For example, a sensor set up to monitor bearings on a conveyor belt might have the position of Left bearing 1 with a position type of Bearing. Amazon Monitron uses Near Field Communications (NFC), a short-range (4 cm or less) wireless technology for communication between two electronic devices. To use Amazon Monitron, you need an iOS or Android 8.0+ smartphone with NFC installed natively. Step 4: Pairing Sensors to an Asset 49 Amazon Monitron Important User Guide The equipment that you want to monitor must be in a healthy state before you pair it to a sensor. Amazon Monitron must establish a baseline for the equipment based on its normal state so that it can later determine abnormalities. To pair a sensor with an asset 1. Attach your sensor in the correct position, as described in Step 3: Attach Sensors . You can also attach the sensor after pairing it to the asset in this step 4. 2. Make sure that the NFC feature on your smartphone is on and functioning. 3. Open your Amazon Monitron mobile app, and select the Project you want to add the sensors to. 4. 5. From the navigation menu, make sure you're in the correct Site, and then choose Assets. From the Assets list, choose the asset that you just created. 6. On your Asset page, choose Add position. 7. On the Add position page, do the following: a. b. For Name, add a name for your position. For Type, choose the Type of position that best fits the location that you're going to monitor: • Bearing • Compressor • Fan • Gearbox • Motor • Pump • Other Note After you pair the sensor, you can't change the position type. Step 4: Pairing Sensors to an Asset 50 Amazon Monitron User Guide c. For Class, choose the machine class of the asset from the four available. Note Asset machine class is based on ISO 20816 Standards. Amazon Monitron administrators can also create custom machine asset classes for all positions within |
AmazonMonitronUG-015 | AmazonMonitronUG.pdf | 15 | Name, add a name for your position. For Type, choose the Type of position that best fits the location that you're going to monitor: • Bearing • Compressor • Fan • Gearbox • Motor • Pump • Other Note After you pair the sensor, you can't change the position type. Step 4: Pairing Sensors to an Asset 50 Amazon Monitron User Guide c. For Class, choose the machine class of the asset from the four available. Note Asset machine class is based on ISO 20816 Standards. Amazon Monitron administrators can also create custom machine asset classes for all positions within a project. For more information about machine classes and customizing them, see Assets. 8. Choose Next. You'll be prompted to add sensors. For information on how to add sensors, see Sensors. 9. Choose Pair sensor. 10. Hold your phone close to the sensor to register it. A progress bar shows when registration is complete. It can take a few moments for the sensor to be commissioned. If you have trouble pairing the sensor, see Pairing Your Sensor for more information. Step 4: Pairing Sensors to an Asset 51 Amazon Monitron Tip User Guide If your smartphone fails to detect the sensor, try holding it so that the NFC antenna is close to the sensor. For iPhone models, the antenna is located at the top edge of the device. For Android models, the antenna location varies. The following resources might help you locate the NFC antenna on an Android device: • NFC detection area (Samsung) • Pixel phone hardware diagram On the Assets page, the sensor is now paired to the asset and is identified by its position. Understanding warnings and alerts Note This section focuses on using the Amazon Monitron mobile app. To learn about the Amazon Monitron web app, see Understanding sensor measurements in the Amazon Monitron User Guide. After a sensor is paired to an asset, Amazon Monitron starts monitoring the asset's condition. When it detects an abnormal machine condition, it sends you a notification ( and changes the asset state. The alert notification is generated using a combination of machine learning and ISO 20816 standards for machine vibration. To monitor the data and respond to alerts about abnormalities, you use the Amazon Monitron mobile app. Your administrator will send you an email with information about how to log in for the first time ) and connect to your project. Topics • Step 1: Understanding asset health Understanding warnings and alerts 52 Amazon Monitron • Step 2: Viewing asset conditions • Step 3: Viewing and acknowledging a machine abnormality • Step 4: Resolving a machine abnormality • Step 5: Muting and unmuting alerts Step 1: Understanding asset health User Guide To monitor assets using the Amazon Monitron mobile app, start with the Assets list. This list is displayed when you open the mobile app. Each asset in your project or site is listed in the Assets list. On the Assets list page, each asset shows an icon indicating its health. The following table describes these icons. Step 1: Understanding asset health 53 Amazon Monitron Icon User Guide Health state Healthy state: The status of all sensor positions on the asset is healthy. Warning state: A warning has been triggered for one of the positions of this asset, indicating that Amazon Amazon Monitron has detected early signs of potential failure. Amazon Amazon Monitron identifie s warning conditions by analyzing equipment vibration and temperatu re, using a combination of machine learning and ISO vibration standards. Alarm state: Once an asset has been placed in a warning state, Amazon Monitron will continue to monitor it. Again, Amazon Monitron is using a combination of machine learning and vibration ISO standards. If the condition of the asset gets significa ntly worse, Amazon Amazon Monitron will escalate by sending an Alarm notificat ion when it detects that the equipment condition has significantly worsened. We recommend investigating the Step 1: Understanding asset health 54 Amazon Monitron Icon No sensor User Guide Health state issue at the earliest opportuni ty. An equipment failure might occur if the issue isn't addressed. Maintenance state: One of the asset's sensors is in the maintenance state. The alarm state of the asset has been acknowledged by a technicia n, but not yet addressed. No sensor: At least one position on the asset doesn't have a sensor paired to it. When you choose an asset, the app displays the health status of each underlying sensor position. Step 1: Understanding asset health 55 Amazon Monitron User Guide The following table describes the position status indicators. Status State The position is healthy: All measured values are within their normal range. A warning has been triggered for this position indicatin g early signs of a potential failure condition. We recommend that you monitor the |
AmazonMonitronUG-016 | AmazonMonitronUG.pdf | 16 | the asset has been acknowledged by a technicia n, but not yet addressed. No sensor: At least one position on the asset doesn't have a sensor paired to it. When you choose an asset, the app displays the health status of each underlying sensor position. Step 1: Understanding asset health 55 Amazon Monitron User Guide The following table describes the position status indicators. Status State The position is healthy: All measured values are within their normal range. A warning has been triggered for this position indicatin g early signs of a potential failure condition. We recommend that you monitor the equipment closely and initiate an investigation during an upcoming planned maintenance. Step 1: Understanding asset health 56 Amazon Monitron Status No sensor User Guide State An alarm has been triggered for this position, indicating that the machine vibration or temperature is out of the normal range at this position. We recommend investiga ting the issue at the earliest opportunity. An equipment failure might occur if the issue isn't addressed. The alarm state of the position has been acknowled ged by a technician, but not yet addressed. The position doesn't have a sensor paired to it. When an issue is raised for an individual position, the status changes for that position and for the asset as a whole. Step 2: Viewing asset conditions Viewing assets is more than simply understanding the icons that show the asset and position health status. It is often useful to see the data collected by the sensors yourself. To view sensor data in the Amazon Monitron mobile app 1. In the Assets list, choose the asset you want to view. 2. Choose the position with the data that you want to view. 3. Under the Vibration and Temperature tabs, choose the chart of recent sensor data and the level of detail that you want to see. You can choose separate versions for different time periods (1 day, 1 week, 2 weeks, 1 month, and so on). Step 2: Viewing asset conditions 57 Amazon Monitron User Guide Step 2: Viewing asset conditions 58 Amazon Monitron User Guide Step 3: Viewing and acknowledging a machine abnormality The longer Amazon Monitron monitors a position, the more it fine-tunes its baseline and increases its accuracy. When an Alarm or a Warning is triggered, Amazon Monitron sends a notification to the mobile app that is displayed as an icon in the upper right of your screen ( ). Choosing the notification icon opens the Notifications page, which lists all pending notifications. When you receive a notification, you must view and acknowledge it. This doesn't fix the issue with the asset, it just lets Amazon Monitron know that you are aware of it. To view and acknowledge an abnormality 1. On the Assets list, choose the asset with the alarm. Step 3: Viewing and acknowledging a machine abnormality 59 Amazon Monitron User Guide Step 3: Viewing and acknowledging a machine abnormality 60 Amazon Monitron User Guide 2. Choose the position with the alarm to view the issue. 3. To confirm that you are aware of the issue, choose Acknowledge. Note that the text on the following screens also indicates whether the alert notification was triggered based on the equipment's vibration or temperature, or by the vibration ISO thresholds or machine learning models. This information can be used by technicians to investigate and fix the issue. After an abnormality has been acknowledged and repaired, resolve the issue in the mobile app. Step 3: Viewing and acknowledging a machine abnormality 61 Amazon Monitron User Guide Step 3: Viewing and acknowledging a machine abnormality 62 Amazon Monitron User Guide The status of the asset changes to: After the alarm has been acknowledged, the abnormality can be examined and fixed as appropriate. Step 4: Resolving a machine abnormality Resolving an abnormality returns the sensor to healthy status and provides information about the issue to Amazon Monitron so it can better determine when a failure might occur in the future. For information about failure modes and causes, and how to resolve abnormalities, see Resolving a Machine Abnormality in the Amazon Monitron User Guide. To resolve an abnormality 1. In the Assets list, choose the asset with the issue. 2. Choose the position with the resolved abnormality. 3. Choose Resolve. 4. 5. 6. For Failure mode, choose one of the available types. For Failure cause, choose the cause. For Action taken choose the action taken. 7. Choose Submit. In the Assets list, the asset status returns to Healthy. Step 5: Muting and unmuting alerts You can choose to mute and unmute alerts (alarms and warnings) for a position. Topics • Muting alerts • Unmuting alerts Step 4: Resolving a machine abnormality 63 Amazon Monitron Muting alerts User Guide ISO thresholds apply broadly to large classes of equipment. Therefore, |
AmazonMonitronUG-017 | AmazonMonitronUG.pdf | 17 | issue. 2. Choose the position with the resolved abnormality. 3. Choose Resolve. 4. 5. 6. For Failure mode, choose one of the available types. For Failure cause, choose the cause. For Action taken choose the action taken. 7. Choose Submit. In the Assets list, the asset status returns to Healthy. Step 5: Muting and unmuting alerts You can choose to mute and unmute alerts (alarms and warnings) for a position. Topics • Muting alerts • Unmuting alerts Step 4: Resolving a machine abnormality 63 Amazon Monitron Muting alerts User Guide ISO thresholds apply broadly to large classes of equipment. Therefore, when detecting the potential failure of a specific asset, you may consider other factors as well. For example, you can mute a notification generated by ISO vibration thresholds if you assess that your equipment is still healthy when the alert is raised. You also can mute alerts (alarms and warnings) by providing the ‘No failure detected’ feedback for the ‘Failure mode’ while closing the alert. Note that Amazon Monitron will continue to notify users of potential failures detected based on machine learning, even when notifications based on ISO thresholds are muted. Muting alerts on mobile app The following images show you how to mute alerts on the Amazon Monitron mobile app. Muting alerts on web app The following images show you how to mute alerts on the Amazon Monitron web app. Step 5: Muting and unmuting alerts 64 Amazon Monitron User Guide Step 5: Muting and unmuting alerts 65 Amazon Monitron Unmuting alerts User Guide You can choose to unmute alerts (alarms and warnings) at any time. When unmuting alerts, you can choose from the following options. Available options • Resume all alerts (alarms and warnings) • Resume alarms but keep warnings muted • Resume only alarms • Resume only warnings Resume all alerts (alarms and warnings) If you've muted both alarms and warnings, you can unmute them. Resume all alerts on mobile Step 5: Muting and unmuting alerts 66 Amazon Monitron Resume all alerts on web User Guide Step 5: Muting and unmuting alerts 67 Amazon Monitron User Guide Resume alarms but keep warnings muted If you've muted both alarms and warnings, you can unmute alarms and keep warnings muted. Resume alarms keeping warnings muted on the mobile app Step 5: Muting and unmuting alerts 68 Amazon Monitron User Guide Resume alarms keeping warnings muted on the web app Step 5: Muting and unmuting alerts 69 Amazon Monitron User Guide Resume only alarms If you've muted alarms, you can unmute them. Resume alarms on mobile app Step 5: Muting and unmuting alerts 70 Amazon Monitron Resume alarms on web app User Guide Step 5: Muting and unmuting alerts 71 Amazon Monitron Resume only warnings If you've muted warnings, you can choose to resume them. Resume warnings on mobile app User Guide Step 5: Muting and unmuting alerts 72 Amazon Monitron Resume warnings on web app User Guide Step 5: Muting and unmuting alerts 73 Amazon Monitron User Guide Step 5: Muting and unmuting alerts 74 Amazon Monitron Projects User Guide A Project is the foundation for using Amazon Monitron. A project is where your team sets up the gateways, assets, and sensors that Amazon Monitron uses to detect the abnormal conditions that can lead to equipment failure. An Amazon Monitron Project is structured like this: Project → site or sites → assets → positions → sensors You can't share these resources between projects. Before you begin creating a project, we recommend that you consider your project's needs. Make sure that it contains all the resources required to predict the maintenance needs for all your assets. Only a project-level admin user or IT manager can create, update, and delete projects and use the Amazon Monitron console for those tasks. Topics • Creating a project • Using tags with your project • Updating a project • Switching between projects • Deleting a project • Additional project tasks Creating a project Although an AWS account can have multiple Amazon Monitron projects, typically you have one per account. The project name must be unique in your AWS account and AWS Region. To create a project 1. Open the Amazon Monitron console at https://console.aws.amazon.com/monitron. 2. Choose Create Project. 3. Under Project Details, for Project name, enter a name that: • Is unique in the current account Creating a project 75 Amazon Monitron User Guide • Consists of uppercase and lowercase letters, numbers, punctuation marks, and spaces • Is between 1 and 60 characters 4. By default, Amazon Monitron uses an AWS owned key to encrypt your project through the AWS Key Management Service (AWS KMS). If you want to use a different AWS KMS key, choose Custom encryption settings (advanced) under Data encryption and do one of the following: • If you already have |
AmazonMonitronUG-018 | AmazonMonitronUG.pdf | 18 | 2. Choose Create Project. 3. Under Project Details, for Project name, enter a name that: • Is unique in the current account Creating a project 75 Amazon Monitron User Guide • Consists of uppercase and lowercase letters, numbers, punctuation marks, and spaces • Is between 1 and 60 characters 4. By default, Amazon Monitron uses an AWS owned key to encrypt your project through the AWS Key Management Service (AWS KMS). If you want to use a different AWS KMS key, choose Custom encryption settings (advanced) under Data encryption and do one of the following: • If you already have a AWS KMS key that you want to use, under Choose an AWS AWS KMS key, choose the key or enter the key's Amazon Resource Name (ARN). • If you want to create a key, choose Create an AWS AWS KMS key. This takes you to the AWS KMS console so you can set up a custom key. 5. (Optional) To add a tag to the project, enter a key-value pair under Tags and then choose Add tag. To remove this tag before creating the project, choose Remove tag. 6. Choose Next to create the project. Using tags with your project A tag is a key-value pair that you can use to categorize your projects. For example, if you have multiple projects, you might categorize them by purpose, owner, location, or any other factor. Use tags to: • Organize your projects. You can search and filter by tag. For example, you could add tags such as ‘test lab’ or ‘paint shop' to easily find those projects. • Identify and organize your AWS resources. Many AWS services support tagging, so you can assign the same tag to resources in different services to indicate that the resources are related. For example, you can tag a project and the Amazon Simple Storage Service (Amazon S3) bucket that stores related data with the same tag. • Control access to your resources. You can use tags in AWS Identity and Access Management (IAM) polices that control access to Amazon Monitron projects. You can attach these policies to an IAM role or user to enable tag-based access control. For more information, see Controlling access using tags in the IAM user Guide. Each tag key must be unique within a project. The following restrictions also apply to Amazon Monitron project tags: Using tags with your project 76 Amazon Monitron User Guide • The maximum number of tags per project is 50. • The maximum length of a tag key is 128 characters. • The maximum length of a tag value is 256 characters. • Valid characters for keys and values are a–z, A–Z, space, _ . : / = + - and @. • Tag keys and values are case sensitive. • The aws: prefix is reserved for AWS use. • If you plan to use your tagging schema across multiple services and resources, remember that other services might have different restrictions for valid characters. Refer to the documentation for that service. Topics • Adding a tag to a project when you create it • Adding a tag to a project after it’s been created • Modifying or removing a tag Adding a tag to a project when you create it To add a tag to a project when creating it 1. Open the Amazon Monitron console at https://console.aws.amazon.com/monitron . 2. Choose Create Project. 3. 4. In the navigation pane, choose the project you want. Expand the Tags section. 5. Choose Add new tag. Adding a tag to a project when you create it 77 Amazon Monitron User Guide 6. Enter the key-value pair for your tag. The key must be unique for the project. The value is optional. 7. Choose Add new tag. 8. 9. To add more tags, repeat steps 2 and 3. To remove a tag, choose Remove. 10. Remove blank tag entries and then choose Next. Adding a tag to a project when you create it 78 Amazon Monitron User Guide Adding a tag to a project after it’s been created You can add a tag to a project on the project detail page. To add a tag to an existing project 1. Open the Amazon Monitron console at https://console.aws.amazon.com/monitron . 2. Choose Create Project. 3. In the navigation pane, choose Projects, and then choose the project you want. 4. Under Tags, choose Manage tags. 5. Choose Add new tag Adding a tag to a project after it’s been created 79 Amazon Monitron User Guide 6. Enter the key-value pair for your tag. Note Remember that the key must be unique for the project. The value is optional. 7. Choose Save. Modifying or removing a tag You can modify a tag value, but not a tag key. To change a tag key, remove |
AmazonMonitronUG-019 | AmazonMonitronUG.pdf | 19 | Open the Amazon Monitron console at https://console.aws.amazon.com/monitron . 2. Choose Create Project. 3. In the navigation pane, choose Projects, and then choose the project you want. 4. Under Tags, choose Manage tags. 5. Choose Add new tag Adding a tag to a project after it’s been created 79 Amazon Monitron User Guide 6. Enter the key-value pair for your tag. Note Remember that the key must be unique for the project. The value is optional. 7. Choose Save. Modifying or removing a tag You can modify a tag value, but not a tag key. To change a tag key, remove the tag, then create a new tag with a different key. You can also remove any tag. You modify or remove tags on the project detail page. Modifying or removing a tag 80 Amazon Monitron To modify or remove a tag User Guide 1. Open the Amazon Monitron console at https://console.aws.amazon.com/monitron. 2. Choose Create Project. 3. In the navigation pane, choose Projects, and then choose the project you want. 4. Under Tags, choose Manage tags. 5. To modify the tag value, make the change. To remove the tag, choose Remove next to the tag. 6. Choose Save. Updating a project Only the project name can be edited using this procedure. The list of Admin users can also be changed, but you do this using the edit users process. To edit a project 1. Open the Amazon Monitron console at https://console.aws.amazon.com/monitron . 2. Choose Create Project. 3. 4. In the navigation pane, choose the project you want to change. From the Projects list, choose the project you want to edit. 5. Choose Edit project. 6. Edit the project name. 7. Choose Save. Updating a project 81 Amazon Monitron User Guide Switching between projects You can switch between Amazon Monitron projects from both your mobile and web app to manage your resources. Note You can only be signed in to a single project at a time. When you switch projects you are automatically logged out from the project you were actively using. When you log into a project using your account credentials, Amazon Monitron automatically adds your project to the Amazon Monitron projects page to make tracking easier. You can also choose to add projects manually to your projects page using the project URL in your Amazon Monitron invitation email. When you add a project, it gets saved only on the platform you are adding it on. A project added or saved on the Amazon Monitron web app doesn't automatically get saved on the Amazon Monitron mobile app unless you also add it to the web app. Topics • Switching between projects in the web app • Switching between projects in the mobile app Switching between projects in the web app To switch between projects in the web app 1. Open the Amazon Monitron console at https://console.aws.amazon.com/monitron . 2. Choose Open in Amazon Monitron web app. Switching between projects 82 Amazon Monitron User Guide 3. 4. Enter your Username and Password on the Sign in screen. From the Assets list page, select your account details dropdown menu, and then choose View projects. Switching between projects in the web app 83 Amazon Monitron User Guide 5. If you want to add a project, choose Add project and enter your project link url. 6. If you want to switch between projects, choose the project you want to view from the projects list. You will see this message before you switch. Switching between projects in the web app 84 Amazon Monitron User Guide Switching between projects in the mobile app To switch between projects in the mobile app 1. Open the Amazon Monitron mobile app and login using your username and password. Switching between projects in the mobile app 85 Amazon Monitron User Guide 2. From the Assets list page, select your account details dropdown menu, and then choose View projects. Switching between projects in the mobile app 86 Amazon Monitron User Guide 3. If you want to add a project, choose Add project and enter your project link url. Switching between projects in the mobile app 87 Amazon Monitron User Guide 4. If you want to switch between projects, choose the project you want to view from the projects list. You will see this message before you switch. Switching between projects in the mobile app 88 Amazon Monitron User Guide Deleting a project With the deleteProject operation, you must have the AWS IAM Identity Center permissions for deletion. Without these permissions, the console's delete project functionality will still remove the Deleting a project 89 Amazon Monitron User Guide project. However, it will not remove the resources from IAM Identity Center and you may end up with dangling references on IAM Identity Center. To delete a project 1. Open the Amazon Monitron console at https://console.aws.amazon.com/monitron |
AmazonMonitronUG-020 | AmazonMonitronUG.pdf | 20 | to view from the projects list. You will see this message before you switch. Switching between projects in the mobile app 88 Amazon Monitron User Guide Deleting a project With the deleteProject operation, you must have the AWS IAM Identity Center permissions for deletion. Without these permissions, the console's delete project functionality will still remove the Deleting a project 89 Amazon Monitron User Guide project. However, it will not remove the resources from IAM Identity Center and you may end up with dangling references on IAM Identity Center. To delete a project 1. Open the Amazon Monitron console at https://console.aws.amazon.com/monitron . 2. Choose Create Project. 3. 4. In the navigation pane, choose Projects. From the Projects list, choose the project you want to delete. 5. Choose Delete Project. 6. Enter Delete in the confirmation box to confirm the deletion. If the project contains any active assets, sensors or gateways, you have to remove them before deleting the project. If this is the case, the confirmation box and option to delete don't appear. If there are active assets or sensors that need to be removed to delete this project, ask an Admin user do this or do it yourself by logging into the Amazon Monitron mobile app. 7. Choose Delete. Additional project tasks Two common project-related tasks that you might frequently encounter are listing all of your projects and retrieving the details on one specific project. You accomplish both of these tasks using the Amazon Monitron console. To list all projects 1. Open the Amazon Monitron console at https://console.aws.amazon.com/monitron . 2. Choose Create Project. 3. In the navigation pane, choose Projects. The list of projects is displayed under Projects. To get details about a project 1. Open the Amazon Monitron console at https://console.aws.amazon.com/monitron . Additional project tasks 90 Amazon Monitron 2. Choose Create Project. 3. In the navigation pane, choose Projects. The list of projects is displayed under Projects. 4. Choose the project that you want to get details on. User Guide Additional project tasks 91 Amazon Monitron Sites User Guide After setting up a project, you can organize it into sites to make it easier to manage. A site is a collection of assets, gateways, and sensors that share a purpose. Organizing a project into sites is helpful if your project has a large pool of assets, gateways, and sensors. You can use sites to control access and permissions to specific parts of that pool. You can create up to 50 sites within a project, and add up to 100 assets and 200 gateways to each site. Topics • Organizing a project into sites • Controlling access to projects and sites • Creating a site • Changing a site name • Deleting a site • Navigating between projects and sites in the mobile app Organizing a project into sites You can organize a project into sites based on your business needs. For example, you can organize a project in one of the following ways: • No sites at all. Everything is contained in a project, without any sites. This option is best for projects with a few assets and users that you can easily keep track of because it provides the greatest simplicity. • Sites based on geography. Group resources and users by locale, such as by city, building, or areas within a building. For example, you might set up a site for the equipment in a factory test lab. • Sites based on function. Group resources and users by functionality, either by machine functionality or by how they're used in your factory. For example, you might set up a site for all of the conveyor belts involved in moving an item from one side of the factory to the other. • Sites based on organization. Sites represent a specific organizational structure in the company or factory. For example, you might want a single site that includes resources and users assigned to the shipping department. Organizing a project into sites 92 Amazon Monitron User Guide Controlling access to projects and sites To give a user access to all of the resources in a project, including those in all of the project's sites, you add the user to the project. To give a user access to only the resources in a site, add the user to the site. Similarly, to make an asset or sensor available to all of the users who have access to an entire project, add it to the project. To make an asset or sensor available only to a specific site, add it to only that site. Gateways are always accessible to anyone or any sensor in the project. For example: Olga is an admin user associated with the entire project. As a project-level admin user, she can manage users and resources anywhere within the project, including |
AmazonMonitronUG-021 | AmazonMonitronUG.pdf | 21 | give a user access to only the resources in a site, add the user to the site. Similarly, to make an asset or sensor available to all of the users who have access to an entire project, add it to the project. To make an asset or sensor available only to a specific site, add it to only that site. Gateways are always accessible to anyone or any sensor in the project. For example: Olga is an admin user associated with the entire project. As a project-level admin user, she can manage users and resources anywhere within the project, including those within sites A, B, and C. Sam is an admin user associated with Site B. As a site-level admin user, he can manage users and resources within Site B but can't see or manage those within sites A and C. Sensors at Site B can use any gateway within the project. Similarly, if Ed is a project-level technician, he can monitor any sensor in the project. However, Tom, who is a site-level technician for Site C, can see and monitor only sensors at that site. Creating a site To add a site to a project, you must be a project-level admin user. You can create up to 50 sites within a project, and add up to 100 assets and 200 gateways to each site. You can make up to 20 users into admin users or technicians for a site. Topics • To add a site using the mobile app • To add a new site using the web app To add a site using the mobile app 1. Log into the Amazon Monitron mobile app on your smartphone. Make sure that the project name is shown in the upper left of the screen. It is visible on all screens in the mobile app. 2. Choose the menu icon (☰). 3. Choose Sites. 4. Choose Add site. Controlling access to projects and sites 93 Amazon Monitron 5. For Site name, enter a name. 6. Choose Add. The Sites list displays the new site. User Guide To add a new site using the web app 1. Open the project selector dropdown menu from the upper left part of the app window. 2. Choose Add a new site The project-level admin user who creates a site is automatically a site-level admin user for that site. To learn more about adding users, see Adding a user. To add a new site using the web app 94 Amazon Monitron User Guide Changing a site name You can change only a site's name. When you change the name, nothing else (such as historical data or user permissions) changes. Topics • To change a site name using the mobile app • To change a site name using the web app To change a site name using the mobile app 1. Log into the Amazon Monitron mobile app on your smartphone. Make sure that the project name is shown in the upper left of the screen. 2. Choose the menu icon (☰). 3. Choose Sites. 4. Next to the site that you want to rename, choose Actions. 5. Choose Edit site name. 6. Change the site name. The new name is displayed in the Sites list. To change a site name using the web app 1. Choose Sites from the left pane. 2. Select the site that you want to rename. 3. Choose the Edit site name button. Changing a site name 95 Amazon Monitron User Guide Deleting a site Before you can delete a site, you must delete all of the site's assets. The Sites list displays all of the devices and users associated with a site. Topics • To delete a site using the mobile app • To delete a site using the web app To delete a site using the mobile app 1. Log into the Amazon Monitron mobile app using your smartphone. Make sure that the project name is shown in the upper left of the screen. 2. Choose the menu icon (☰). Deleting a site 96 Amazon Monitron 3. Choose Sites. 4. Next to the site that you want to delete, choose Actions. 5. Choose Delete site. User Guide 6. If assets, sensors, gateways, or users are associated with the site, choose X. Then delete those resources before proceeding. If there are no resources associated with the site, skip to the next step. 7. Choose Delete. The site is no longer listed in the Sites list. To delete a site using the web app 1. Choose Sites from the left pane. 2. Select the site that you want to delete. 3. Choose Delete site. To delete a site using the web app 97 Amazon Monitron User Guide Navigating between projects and sites in the mobile app Project-level admin users and project-level technicians |
AmazonMonitronUG-022 | AmazonMonitronUG.pdf | 22 | sensors, gateways, or users are associated with the site, choose X. Then delete those resources before proceeding. If there are no resources associated with the site, skip to the next step. 7. Choose Delete. The site is no longer listed in the Sites list. To delete a site using the web app 1. Choose Sites from the left pane. 2. Select the site that you want to delete. 3. Choose Delete site. To delete a site using the web app 97 Amazon Monitron User Guide Navigating between projects and sites in the mobile app Project-level admin users and project-level technicians can access and manage either project-level or site-level resources. Project-level admin users can add resources and users at either the project or site level. Site admins and site-level technicians have access only to their site. To tell whether you're at the project level or in a specific site, note the name at the top of the app screen. or Project-level admin users and technicians can switch between the project level and the site level or between individual sites. Topics • Switching from project level to site level • Switching from site level to project level Switching from project level to site level To change from project level to site level 1. Log into the Amazon Monitron mobile app on your smartphone. Navigate to the project you want. 2. Choose the project name. Navigating between projects and sites in the mobile app 98 Amazon Monitron User Guide 3. Choose the site that you want to view. Switching from site level to project level To change from site level to project level 1. Log into the Amazon Monitron mobile app on your smartphone. The site name indicates that you are at the site level in the mobile app. 2. Choose the site name. 3. Choose the project name. Switching from site level to project level 99 Amazon Monitron User Guide To change to a different site, choose the site name. Switching from site level to project level 100 Amazon Monitron Gateways User Guide Amazon Monitron uses gateways to transfer the data collected by the Amazon Monitron Sensors to the AWS Cloud. Gateways are positioned in factories within 20 to 30 meters of the sensors. They communicate with the sensors over Bluetooth Low Energy (BLE), and with the AWS Cloud using either Wi-Fi or Ethernet. This topic explains how to install your Ethernet and Wi-Fi gateways. It also explains how to delete an unnecessary gateways. Note Once you've added a gateway to your project, you can edit the gateway's name to help you find it fast. Topics • Ethernet gateways • Wi-Fi gateways Ethernet gateways The Amazon Monitron Ethernet Gateway comes equipped with an RJ-45 socket, so you can connect it to your Ethernet network using a Cat 5e or Cat 6 Ethernet cable. You power your gateway over the Ethernet cable, using Power over Ethernet (POE). Therefore, you need either a router that supports POE or a POE power injector. Ethernet gateways 101 Amazon Monitron User Guide After you have inserted an Ethernet cable into your gateway, put the gateway in commissioning mode by pressing the Config button. To learn about using Amazon Monitron with Wi-Fi gateways, see Wi-Fi gateways. Topics • Reading the LED lights on an Ethernet gateway • Placing and installing an Ethernet gateway Ethernet gateways 102 Amazon Monitron User Guide • Commissioning an Ethernet gateway • Troubleshooting Ethernet gateway detection • Troubleshooting Bluetooth pairing • Resetting the Ethernet gateway to factory settings • Viewing the list of gateways • Viewing Ethernet gateway details • Editing Ethernet gateway name • Deleting an Ethernet gateway • Retrieving MAC address details Reading the LED lights on an Ethernet gateway The LED lights on the top of your Amazon Monitron Ethernet Gateway indicate the status of the gateway. Each gateway has one orange light, one blue light, and one green light. The green light indicates that the power is on. The orange light indicates that the gateway is connected to the Ethernet. The blue light indicates that the gateway's Bluetooth is connected to the sensors. The sequence that the lights display indicates the status of the gateway, as described in the following table. 1 2 3 4 LED sequence Description Solid green light Solid orange light Flashing orange light (slow) The Ethernet gateway is powered on. The gateway is connected to the Ethernet network and the Amazon Monitron backend system. The gateway is attempting to connect to the Ethernet network. Flashing orange light (1 fast/ 1 slow) The gateway is connected to the Ethernet network and is Reading the LED lights on an Ethernet gateway 103 Amazon Monitron User Guide LED sequence Description 5 6 7 8 9 Solid blue light No blue light Orange and blue lights flashing (slowly) Orange and |
AmazonMonitronUG-023 | AmazonMonitronUG.pdf | 23 | table. 1 2 3 4 LED sequence Description Solid green light Solid orange light Flashing orange light (slow) The Ethernet gateway is powered on. The gateway is connected to the Ethernet network and the Amazon Monitron backend system. The gateway is attempting to connect to the Ethernet network. Flashing orange light (1 fast/ 1 slow) The gateway is connected to the Ethernet network and is Reading the LED lights on an Ethernet gateway 103 Amazon Monitron User Guide LED sequence Description 5 6 7 8 9 Solid blue light No blue light Orange and blue lights flashing (slowly) Orange and blue lights flashing (rapidly) No lights attempting to connect to the Amazon Monitron backend system. At least one sensor is communicating with the gateway. No sensors are currently communicating with the gateway. The gateway is powered on, unconfigured (not commissio ned), and not in commissio ning mode (that is, not discoverable or configurable by the mobile app). The gateway is on and in commissioning mode, but not yet linked to any sensors. In commissioning mode, the gateway is discoverable and configurable by Amazon Amazon Monitron, but no sensors can connect yet. The gateway isn't connected to a power source or a firmware update is in progress. 10 Solid orange and blue lights The gateway is starting up. Reading the LED lights on an Ethernet gateway 104 Amazon Monitron User Guide Placing and installing an Ethernet gateway Unlike sensors, an Ethernet gateway doesn't need to be attached to the machines that are being monitored. However, it does need an available Ethernet network through which Amazon Monitron can connect to the AWS Cloud. Topics Placing and installing an Ethernet gateway 105 Amazon Monitron • Where to place a gateway • Installing an Ethernet gateway • Turning on the gateway Where to place a gateway User Guide You can install a gateway anywhere within your work area, depending on its layout. Typically, gateways are mounted on walls, but you can mount them on ceilings, pillars, or in any other location. A gateway must be within 20 to 30 meters of the sensors it will support, and an Ethernet gateway must be close enough to an Ethernet cable to plug in. Note that an Ethernet gateway draws power from the Ethernet cable. Consider these other factors when mounting a gateway: • Mounting the gateway higher than sensors (2 meters or above) can improve coverage. • Keeping an open line of sight between the gateway and sensors improves coverage. • Avoid mounting the gateway on building structures, such as exposed steel beams. They can cause interference with the signal. • Try to work around any equipment that might produce electronic interference with the signal. • If possible, install more than one gateway within transmission distance of your sensors. If a gateway becomes unavailable, the sensors will switch their data transmission to another gateway. Having multiple gateways helps to eliminate data loss. There is no minimum required distance between two gateways. Installing an Ethernet gateway Almost everything you need to install your gateway in your work area is contained in the box that contains the gateway: • The gateway • A wall mounting bracket • Double-sided tape • Four mounting screws Placing and installing an Ethernet gateway 106 Amazon Monitron User Guide To install the gateway, position the wall mounting bracket on the wall or on another location, then mount the gateway on the bracket, Ethernet cable on the downwards side. There are three ways to mount the mounting bracket: screw mounting, tape mounting, and plastic- tie mounting. The method you use depends on whether you're mounting the gateway on a wall or another location, and on the surface material. To mount the bracket, choose one of the following. Screw mounting Typically, you mount the bracket directly to the wall using the mounting screws included in the gateway box. Mount the bracket from the front. You might need to use an expansion plug or toggle bolt (not included) to secure the screw in the wall. Placing and installing an Ethernet gateway 107 Amazon Monitron Tape mounting User Guide A shaped piece of double-sided tape is included in the gateway box. Use it when you can't place a screw into the mounting surface. You can also use it in combination with the other methods of mounting for a more secure installation. Remove the backing on one side of the tape and apply the tape to the back of the wall mounting bracket between the four raised sections. Placing and installing an Ethernet gateway 108 Amazon Monitron User Guide Remove the remaining backing and apply the bracket to the mounting location. Press hard on the bracket to make sure that the tape firmly adheres to the surface. Plastic-tie mounting To mount a gateway to a smaller non-wall location, |
AmazonMonitronUG-024 | AmazonMonitronUG.pdf | 24 | a screw into the mounting surface. You can also use it in combination with the other methods of mounting for a more secure installation. Remove the backing on one side of the tape and apply the tape to the back of the wall mounting bracket between the four raised sections. Placing and installing an Ethernet gateway 108 Amazon Monitron User Guide Remove the remaining backing and apply the bracket to the mounting location. Press hard on the bracket to make sure that the tape firmly adheres to the surface. Plastic-tie mounting To mount a gateway to a smaller non-wall location, such as a pillar or fence, use cable ties (also known as zip ties) to fasten the wall mounting bracket. Put the ties through the holes in the four raised sections on the back of the bracket, wrap them around the mounting location, and pull tight. Placing and installing an Ethernet gateway 109 Amazon Monitron User Guide After the bracket is mounted, attach the gateway to the bracket. Turning on the gateway 1. With the wall mounting bracket in place, place the gateway against the bracket, with the two plastic hooks on the back of the gateway inserted in the slots at the bottom of the bracket. 2. Press the top of the gateway against the bracket so that the plastic hooks on the back of the gateway latch into the top of the bracket. Note Install the gateway with the Ethernet cable going downwards. If you have a problem with connecting to your gateway, see Troubleshooting Ethernet gateway detection. Commissioning an Ethernet gateway When your gateway is mounted in your factory, you will need access to the Amazon Monitron mobile app to commission it. Amazon Monitron supports only smartphones using Android 8.0+ or iOS 14+ with near field communication (NFC) and Bluetooth. Commissioning an Ethernet gateway 110 Amazon Monitron Topics • To commission a gateway To commission a gateway User Guide 1. If Bluetooth isn't already turned on for your smartphone, turn it on. 2. Position your gateway in the location that works best for communicating with your sensors. The best place to mount your gateway is higher than the sensors and no more than 20 to 30 meters away. For additional help with locating your gateway, see Placing and installing an Ethernet gateway. 3. Plug in the gateway and make sure the network light (yellow) and the Bluetooth light (blue) on the front of your gateway are blinking alternately. 4. Push the Config button on the gateway to put it into commissioning mode. The Bluetooth and network LED lights will start flashing rapidly. 5. Open the mobile app on your smartphone. 6. On the Getting started page or the Gateways page, choose Add gateway. Amazon Monitron scans for the gateway. This can take a few moments. When Amazon Monitron finds the gateway, it displays it in the gateway list. 7. Choose the gateway. Note If you are using an iOS mobile device, and you have previously paired with this particular gateway, then you may need to make your device "forget" the gateway before re-pairing. For more information, see Troubleshooting Bluetooth pairing. It can take a few moments for Amazon Monitron to connect to the new gateway. Commissioning an Ethernet gateway 111 Amazon Monitron User Guide If the mobile app continues to try to connect to the gateway without success, see Troubleshooting Ethernet gateway detection. Note When the gateway is successfully connected, Amazon Monitron displays the gateway device ID and MAC ID in the mobile app. 8. After it connects to the gateway, Amazon Monitron will provide two options for you to configure the network connection for your gateway. 9. Choose your network configuation. It can take a few minutes for the gateway to be commissioned and to connect to the network. Commissioning an Ethernet gateway 112 Amazon Monitron User Guide If you have further difficulties making the gateway work, it might be helpful to reset it. For more information, see Resetting the Ethernet gateway to factory settings. a. If you choose automatic (DHCP), Amazon Monitron will automatically configure the network to connect to the gateway. b. If you choose manual, enter your IP address, subnet mask, router, preferred DNS server, and alternate DNS server (optional) information. Then choose connect. Troubleshooting Ethernet gateway detection When you add a gateway to your project or site, as soon as you choose Add Gateway, the Amazon Monitron mobile app starts scanning for the gateway. If the app can't find the gateway, try the following troubleshooting tips. Troubleshooting Ethernet gateway detection 113 Amazon Monitron User Guide • Make sure that the gateway is powered up. Check the small green light near the upper right corner of the gateway. If it's on, the gateway has power. If the gateway has no power, check the |
AmazonMonitronUG-025 | AmazonMonitronUG.pdf | 25 | DNS server, and alternate DNS server (optional) information. Then choose connect. Troubleshooting Ethernet gateway detection When you add a gateway to your project or site, as soon as you choose Add Gateway, the Amazon Monitron mobile app starts scanning for the gateway. If the app can't find the gateway, try the following troubleshooting tips. Troubleshooting Ethernet gateway detection 113 Amazon Monitron User Guide • Make sure that the gateway is powered up. Check the small green light near the upper right corner of the gateway. If it's on, the gateway has power. If the gateway has no power, check the following: • Is the Ethernet cable firmly seated in the RJ-45 socket? • Is the router at the other end of the Ethernet cable functioning properly? • Is the Ethernet cable working? To test this, try using the cable with another gateway. • Is the RJ-45 socket clean? Be sure to also check the socket at the other end of the Ethernet cable. • Make sure the gateway is in configuration mode. The Amazon Monitron mobile app finds a new gateway only when it's in configuration mode. When you turn a gateway on, the Bluetooth and Network LED lights blink slowly, alternating orange and blue. When you press the Config button to enter commissioning mode, they blink rapidly, again alternating orange and blue. • If the LEDs show any sequence other than slow blinking before you press the button, the gateway might not go into configuration mode. In this case, reset the gateway by pressing the Reset button. • Make sure your smartphone's Bluetooth is working. The gateway connects to your smartphone using Bluetooth, so it's a potential source of interruption. Check the following: • Is you smartphone's Bluetooth on and working? Try switching it off and on. If that doesn't help, restart your phone and check again. • Are you within your smartphone's Bluetooth range? Bluetooth range is relatively short, usually less than 10 meters, and its reliability can vary dramatically. Troubleshooting Ethernet gateway detection 114 Amazon Monitron User Guide • Is there anything that might be interfering electronically with the Bluetooth signal? • Make sure this gateway is not already commissioned to any of your projects. The device must be deleted from all existing projects before commissioning. If none of these actions resolves the issue, try the following: • View and copy your gateway MAC address and get in touch with your IT admin. See Retrieving MAC address details. • Log out of the mobile app and restart it. • Perform a factory reset of the gateway by holding down Config and pressing Reset. Troubleshooting Bluetooth pairing You may find yourself attempting to pair your iOS mobile device with a gateway that it has already paired with. This could happen because the gateway has changed locations, or because the general configuration of your Amazon Monitron site has been altered. In that case, tell your iOS device to "forget" its Bluetooth connection with the gateway. Topics • To unpair a gateway from your device To unpair a gateway from your device 1. On your iOS device, choose Settings. 2. On your Settings screen, choose Bluetooth. 3. On the Bluetooth screen, choose the information icon next to the name of your Amazon Monitron Gateway. 4. On the next screen, choose Forget This Device. Resetting the Ethernet gateway to factory settings If you re-use a gateway that was deleted from Amazon Monitron, use the commissioning button to reset the gateway to factory settings. This prepares the gateway to be used again for Amazon Monitron. 115 Amazon Monitron Topics • Resetting the Ethernet gateway to factory settings (option 1) • Resetting the Ethernet gateway to factory settings (option 2) User Guide Resetting the Ethernet gateway to factory settings (option 1) 1. Unplug the Ethernet cable from the gateway. 2. Hold down the Config button. 3. Plug the Ethernet cable back into the gateway. When the LED lights start slowly blinking, alternating orange and blue, release the Config button. The gateway is reset. Resetting the Ethernet gateway to factory settings (option 2) 1. Hold down the Config button. 2. Press the reset button. 3. When the led lights start slowly blinking, alternating orange and blue, release both buttons. Viewing the list of gateways This page describes how to list your gateways in the Amazon Monitron app. Topics • To list your gateways list using the mobile app • To list your gateways using the web app To list your gateways list using the mobile app 1. Use your smartphone to log in to the Amazon Monitron mobile app. 2. Choose the menu icon in the upper left of the screen. 3. Choose Gateways. Viewing the list of gateways 116 Amazon Monitron User Guide A list of all gateways associated with the project is |
AmazonMonitronUG-026 | AmazonMonitronUG.pdf | 26 | and blue, release both buttons. Viewing the list of gateways This page describes how to list your gateways in the Amazon Monitron app. Topics • To list your gateways list using the mobile app • To list your gateways using the web app To list your gateways list using the mobile app 1. Use your smartphone to log in to the Amazon Monitron mobile app. 2. Choose the menu icon in the upper left of the screen. 3. Choose Gateways. Viewing the list of gateways 116 Amazon Monitron User Guide A list of all gateways associated with the project is displayed. To list your gateways using the web app 1. Choose Gateways from the left nav. 2. The gateway list appears in the right pane. Viewing the list of gateways 117 Amazon Monitron User Guide Viewing Ethernet gateway details You can view gateway details on your mobile or web app. The following gateway details are viewable: • IP address • Firmware version • Last time commissioned Note You can also view and copy gateway MAC addresses. See Retrieving MAC address details. You can view sensor details on both the mobile and web app. The following section shows you how. Topics • To view Ethernet gateway details in the mobile app • To view Ethernet gateway details in the web app To view Ethernet gateway details in the mobile app 1. From the Gateways list, choose the gateway whose details you want to view. Viewing Ethernet gateway details 118 Amazon Monitron User Guide 2. From the options box that pops open, select View gateway details. Viewing Ethernet gateway details 119 Amazon Monitron User Guide 3. The Gateway details page is displayed. Viewing Ethernet gateway details 120 Amazon Monitron User Guide To view Ethernet gateway details in the web app 1. From the Gateways list, choose the gateway whose details you want to view. Viewing Ethernet gateway details 121 Amazon Monitron User Guide 2. The Gateway details page is displayed. Editing Ethernet gateway name You can change the display name for your Ethernet gateway to find it faster. To edit a gateway name, open your web or mobile app and do the following. Topics • To edit an Ethernet gateway name To edit an Ethernet gateway name 1. Select the gateway name you want to edit from the Gateways page. Editing Ethernet gateway name 122 Amazon Monitron User Guide Web app view Mobile app view 2. A pop-up will appear prompting you to add a customized name for the gateway. Editing Ethernet gateway name 123 Amazon Monitron User Guide Web app view Mobile app view 3. Enter the new name for the gateway and choose Save. Editing Ethernet gateway name 124 Amazon Monitron User Guide Web app view Mobile app view 4. You will see a success message confirming the new gateway name. Editing Ethernet gateway name 125 Amazon Monitron User Guide Web app view Mobile app view Deleting an Ethernet gateway Sensors need a gateway to relay their data to the AWS Cloud. Deleting a gateway might cause some sensors to lose their connection. Exercise caution before deleting a gateway. Deleting an Ethernet gateway 126 Amazon Monitron User Guide When you delete a gateway, sensors switch their connection to another gateway that is within range, if there is one, and data transmission from the sensor continues uninterrupted. If no gateway is within range, data transmission is interrupted and the data might be lost. When you delete a gateway which is currently offline, you must perform a factory reset of the device before commissioning it again. Topics • Deleting an Ethernet gateway using the mobile app • Deleting an Ethernet gateway using the web app Deleting an Ethernet gateway using the mobile app 1. Using the mobile app, navigate to the Gateways page. 2. Choose the vertical ellipses icon ( next to the gateway that you want to delete. 3. Choose Delete Gateway. 4. Choose Delete again. Deleting an Ethernet gateway using the web app 1. Navigate to the list of Wi-Fi gateways. 2. Select the gateway from the table. 3. Choose Delete gateway. Retrieving MAC address details ) To retrieve your Amazon Monitron gateway's Media Access Control (MAC) address, you can scan the QR code on the gateway device with your mobile phone. Amazon Monitron returns both the MAC address and gateway ID when you scan the QR code. If you are an IT admin, you can use the scanned MAC address to ensure gateway devices are configured with the correct network settings before they are commissioned. If you are a technician Retrieving MAC address details 127 Amazon Monitron User Guide commissioning gateways, you can use the scanned MAC address to troubleshoot any networking issues with your IT admin. Note Retrieving MAC addresses by scanning QR codes is only supported for |
AmazonMonitronUG-027 | AmazonMonitronUG.pdf | 27 | scan the QR code on the gateway device with your mobile phone. Amazon Monitron returns both the MAC address and gateway ID when you scan the QR code. If you are an IT admin, you can use the scanned MAC address to ensure gateway devices are configured with the correct network settings before they are commissioned. If you are a technician Retrieving MAC address details 127 Amazon Monitron User Guide commissioning gateways, you can use the scanned MAC address to troubleshoot any networking issues with your IT admin. Note Retrieving MAC addresses by scanning QR codes is only supported for the Amazon Monitron mobile app. The following procedure shows you how to retrieve your gateway device's MAC address. 1. Navigate to the Gateways page. 2. Select the scan icon. Retrieving MAC address details 128 Amazon Monitron User Guide 3. Amazon Monitron will display a message explaining what scanning a QR code will do. Select Continue. 4. On the Scan QR Code page, scan the gateway QR code using your mobile phone camera. When the scan successfully completes, Amazon Monitron displays the Gateway ID and MAC address on the Scan QR Code page in the mobile app. Retrieving MAC address details 129 Amazon Monitron User Guide You can also select the copy icon ( to copy the MAC address. Note ) If not already enabled, Amazon Monitron may need permissions to access your camera to scan the QR code. These permissions must be enabled from the settings page Retrieving MAC address details 130 Amazon Monitron User Guide of your mobile device before you can successfully scan a device QR code. Amazon Monitron will prompt you to enable camera access during the scanning process if permissions haven't already been granted. On Android devices On iOS devices Retrieving MAC address details 131 Amazon Monitron User Guide Wi-Fi gateways This topic explains how to install your Wi-Fi gateway. It also explains how to delete an unnecessary gateway. To learn about using Amazon Monitron with Ethernet gateways, see Ethernet gateways. Wi-Fi gateways 132 Amazon Monitron User Guide The Amazon Monitron gateway is easy to install and operate. After plugging in the power cable, you can put the gateway in commissioning mode by pressing the commissioning button. Topics • Reading the LED lights on a Wi-Fi gateway • Placing and installing a Wi-Fi gateway • Commissioning a Wi-Fi gateway • Troubleshooting Wi-Fi gateway detection • Troubleshooting Bluetooth pairing • Resetting the Wi-Fi gateway to factory settings • Viewing the list of gateways • Viewing Wi-Fi gateway details • Editing Wi-Fi gateway name • Deleting a Wi-Fi gateway • Retrieving MAC address details Reading the LED lights on a Wi-Fi gateway The LED lights on the top of your Amazon Monitron gateway indicate the status of the gateway. Each LED light has one orange light and one blue light. The orange light indicates that the gateway is connected to a Wi-Fi network. The blue light indicates that the gateway's Bluetooth is connected to the sensors. Reading the LED lights on a Wi-Fi gateway 133 Amazon Monitron User Guide The sequence that the lights display indicates the status of the gateway, as described in the following table. 1 2 3 4 5 LED sequence Description Solid green light Solid orange light Flashing orange light (slow) Flashing orange light (1 fast/ 1 slow) Solid blue light The Wi-Fi gateway is powered on. The gateway is connected to the Wi-Fi network and the Amazon Monitron backend system. The gateway is attempting to connect to the Wi-Fi network. The gateway is connected to the Wi-Fi network and is attempting to connect to the Amazon Monitron backend system. At least one sensor is communicating with the gateway. Reading the LED lights on a Wi-Fi gateway 134 Amazon Monitron User Guide LED sequence Description 6 7 8 9 No blue light Orange and blue lights flashing (slowly) Orange and blue lights flashing (rapidly) No lights No sensors are currently communicating with the gateway. The gateway is powered on, unconfigured (not commissio ned), and not in commissio ning mode (that is, not discoverable or configurable by the mobile app). The gateway is on and in commissioning mode, but not yet linked to any sensors. In commissioning mode, the gateway is discoverable and configurable by Amazon Amazon Monitron, but no sensors can connect yet. The gateway is not connected to a power source or a firmware update is in progress. 10 Solid orange and blue lights The gateway is starting up. Placing and installing a Wi-Fi gateway Unlike sensors, a Wi-Fi gateway doesn't need to be attached to the machines that are being monitored. However, it does need an available Wi-Fi network through which Amazon Monitron can connect to the AWS Cloud. Placing and installing a Wi-Fi gateway 135 Amazon Monitron User Guide Topics • |
AmazonMonitronUG-028 | AmazonMonitronUG.pdf | 28 | In commissioning mode, the gateway is discoverable and configurable by Amazon Amazon Monitron, but no sensors can connect yet. The gateway is not connected to a power source or a firmware update is in progress. 10 Solid orange and blue lights The gateway is starting up. Placing and installing a Wi-Fi gateway Unlike sensors, a Wi-Fi gateway doesn't need to be attached to the machines that are being monitored. However, it does need an available Wi-Fi network through which Amazon Monitron can connect to the AWS Cloud. Placing and installing a Wi-Fi gateway 135 Amazon Monitron User Guide Topics • Choosing a location for your gateway • Mounting the bracket • Mounting the gateway on the bracket Choosing a location for your gateway You can install a gateway almost anywhere within your factory, depending on its layout. Typically, gateways are mounted on a wall, but you can mount them on the ceiling, on pillars, or in almost any other location. A gateway must be within 20 to 30 meters of the sensors it supports. It also must be close enough to a power outlet that it can be plugged in. Consider these other factors when mounting a gateway: • Mounting the gateway higher than sensors (2 meters or more) can improve coverage. • Keeping an open line of sight between the gateway and sensors improves coverage. • Avoid mounting the gateway on building structures, such as exposed steel beams. They can cause interference with the signal. • Try to work around any equipment that might produce electronic interference with the signal. • If possible, install more than one gateway within transmission distance of your sensors. If a gateway becomes unavailable, the sensors will switch their data transmission to another gateway. Having multiple gateways helps to reduce data loss. There is no minimum required distance between two gateways. Placing and installing a Wi-Fi gateway 136 Amazon Monitron Mounting the bracket User Guide To install a gateway, position the wall mounting bracket on the wall or on another location, then mount the gateway on the bracket. Almost everything you need comes in the box that contains the gateway: • The gateway • An AC adapter • AC adapter plugs for the EU, UK, and US • The wall mounting bracket • Double-sided tape • Two mounting screws • One small screw to attach the gateway to the bracket There are three ways to mount the mounting bracket: screw mounting, tape mounting, and plastic- tie mounting. The method you use depends on whether you're mounting the gateway on a wall or another location, and on the surface material. You mount the gateway on the wall mounting bracket through the small screw hole in the center of one of the short sides. To mount the bracket, choose one of the following techniques. Screw mounting Typically, you mount the bracket directly to the wall using the mounting screws included in the gateway box. Mount the bracket from the front. You might need to use an expansion plug or toggle bolt to secure the screw in the wall. An expansion plug or toggle bolt is not included. Placing and installing a Wi-Fi gateway 137 Amazon Monitron User Guide Tape mounting A shaped piece of double-sided tape is included in the gateway box. Use it when you can't place a screw into the mounting surface. You can also use it in combination with the other methods of mounting for a more secure installation. Remove the backing on one side of the tape and apply the tape to the back of the wall mounting bracket between the four raised sections. Placing and installing a Wi-Fi gateway 138 Amazon Monitron User Guide Remove the remaining backing and apply the bracket to the mounting location. Press hard on the bracket to make sure that the tape firmly adheres to the surface. Plastic-tie mounting To mount a gateway to a smaller non-wall location, such as a pillar or fencing, use cable ties (also known as zip ties) to fasten the wall mounting bracket. Put the ties through the holes in the four raised sections on the back of the bracket. wrap them around the mounting location, and pull tight. After the bracket is mounted, attach the gateway to the bracket. Placing and installing a Wi-Fi gateway 139 Amazon Monitron User Guide Mounting the gateway on the bracket In the following procedure, we talk about the "top" and "bottom" of the gateway and the bracket. The two images below demonstrate this standard orientation. As noted below, the device does not have to be upright in order to function. This explanation is just to help you understand the mounting instructions. When the gateway is upright, the Amazon logo on the front of the device is right-side up. The two holes that will reveal |
AmazonMonitronUG-029 | AmazonMonitronUG.pdf | 29 | bracket is mounted, attach the gateway to the bracket. Placing and installing a Wi-Fi gateway 139 Amazon Monitron User Guide Mounting the gateway on the bracket In the following procedure, we talk about the "top" and "bottom" of the gateway and the bracket. The two images below demonstrate this standard orientation. As noted below, the device does not have to be upright in order to function. This explanation is just to help you understand the mounting instructions. When the gateway is upright, the Amazon logo on the front of the device is right-side up. The two holes that will reveal the LEDs are just above the logo, on the right side. The hole for the small screw that will attach the bracket to the gateway is at the top, in the center. On the back of the device, there are two pairs of orange plastic hooks. The large hooks, near the bottom of the device, point downward. The small hooks, near the top of the device, point upward. Placing and installing a Wi-Fi gateway 140 Amazon Monitron User Guide 1. With the wall mounting bracket in place, place the gateway against the bracket. The two large plastic hooks on the back of the gateway should be in the slots at the bottom of the bracket. 2. Press the top of the gateway against the bracket so that the two small plastic hooks on the back of the gateway latch into the top of the bracket. 3. Using the small screw that came with the gateway, fasten the gateway to the bracket through the hole at the top of the gateway. Placing and installing a Wi-Fi gateway 141 Amazon Monitron User Guide 4. Insert the appropriate AC plug into the AC adapter. The following picture shows the US plug attached to the adapter. 5. Plug the AC adapter into the bottom of the gateway and a power outlet. When the LED lights on the gateway blink slowly, alternating orange and blue, the gateway is turned on and ready to be commissioned. Placing and installing a Wi-Fi gateway 142 Amazon Monitron Note User Guide The gateway is designed to be mounted with the small screw securing it at the top. However, installing it upside down doesn’t affect its performance. If you have problems connecting to your gateway, see Troubleshooting Wi-Fi gateway detection. Commissioning a Wi-Fi gateway When your gateway is mounted in your factory, you will need access to the Amazon Monitron mobile app to commission it. Amazon Monitron supports only smartphones using Android 8.0+ or iOS 14+ with Near Field Communication (NFC) and Bluetooth. Topics • To commission a gateway To commission a gateway 1. If Bluetooth isn't already turned on for your smartphone, turn it on. 2. Position your gateway in the location that works best for communicating with your sensors. The best place to mount your gateway is higher than the sensors and no more than 20 to 30 meters away. For additional help with locating your gateway, see Placing and installing a Wi-Fi gateway. 3. Plug in the gateway and make sure the LED lights on top are blinking alternatively yellow and blue. 4. Push the button on the side of the gateway to put it into commissioning mode. The lights will start rapidly blinking. Commissioning a Wi-Fi gateway 143 Amazon Monitron User Guide 5. Open the mobile app on your smartphone. 6. On the Getting Started page or the Gateways page, choose Add gateway. Amazon Monitron scans for the gateway. This can take a few moments. When Amazon Monitron finds the gateway, it displays it in the gateway list. 7. Choose the gateway. Note If you are using an iOS mobile device, and you have previously paired with this particular gateway, you may need to make your device "forget" the gateway before re- pairing. For more information, see Troubleshooting Bluetooth pairing. It can take a few moments for Amazon Monitron to connect to the new gateway. If the mobile app continues to try to connect to the gateway without success, see Troubleshooting Wi-Fi gateway detection. Commissioning a Wi-Fi gateway 144 Amazon Monitron User Guide 8. After it connects to the gateway, Amazon Monitron scans for Wi-Fi networks. Choose the Wi-Fi network that you want to use. 9. Enter your Wi-Fi password, and then choose Connect. It can take a few minutes for the gateway to be commissioned and to connect to the Wi-Fi network. If you have further difficulties, see Resetting the Wi-Fi gateway to factory settings. Troubleshooting Wi-Fi gateway detection When you add a gateway to your project or site, as soon as you choose Add gateway the Amazon Monitron mobile app starts scanning to find it. If the mobile app can't find the gateway, try the following troubleshooting tips. • Make sure that the gateway |
AmazonMonitronUG-030 | AmazonMonitronUG.pdf | 30 | Choose the Wi-Fi network that you want to use. 9. Enter your Wi-Fi password, and then choose Connect. It can take a few minutes for the gateway to be commissioned and to connect to the Wi-Fi network. If you have further difficulties, see Resetting the Wi-Fi gateway to factory settings. Troubleshooting Wi-Fi gateway detection When you add a gateway to your project or site, as soon as you choose Add gateway the Amazon Monitron mobile app starts scanning to find it. If the mobile app can't find the gateway, try the following troubleshooting tips. • Make sure that the gateway is turned on. Check the LED lights—the two small orange and blue lights next to the Amazon symbol on the top of the gateway. If they're on, the gateway has power. If the gateway has no power, check the following: • Is the power cord firmly attached to both the back of the gateway and the power outlet? • Is the power outlet functioning properly? • Is the gateway power cable working? To test this, try using the cable with another gateway. • Is the outlet where the cable plugs into the gateway clean, with no debris stuck inside? Be sure to check both the outlet in the gateway and the connecting end of the cable. • Make sure that the gateway is in commissioning mode. The Amazon Monitron mobile app finds a new gateway only when it's in commissioning mode. When you turn a gateway on, the LED lights blink slowly, alternating orange and blue. When you press the button on the side of the gateway and enter commissioning mode, they blink rapidly, also alternating orange and blue. If the LEDs show any sequence other than slow blinking before you press the button, the gateway Troubleshooting Wi-Fi gateway detection 145 Amazon Monitron User Guide might not go into commissioning mode. In this case, perform a factory reset of the gateway by turning the power off, then pressing and holding down the commissioning button (located on the side) while you turn the power back on. • Make sure your smartphone's Bluetooth is working. The gateway connects to your smartphone using Bluetooth. • Is you smartphone's Bluetooth on and working? Try switching it off and on. If that doesn't help, restart your phone and check again. • Are you within your smartphone's Bluetooth range? Bluetooth range is relatively short, usually less than 10 meters and its reliability can vary dramatically. • Is there anything that might be interfering electronically with the Bluetooth signal? • Make sure the gateway is not already commissioned to any of your projects. The devide must be deleted from all existing projects before commissioning. If none of these actions resolves the issue, try the following: • View and copy your gateway MAC address and get in touch with your IT admin. See Retrieving MAC address details. • Log out of the mobile app and restart it. • Do a factory reset of the gateway by turning the power off, then pressing and holding down the commissioning button on the side while you turn the power back on. Troubleshooting Wi-Fi gateway detection 146 Amazon Monitron User Guide Troubleshooting Bluetooth pairing You may find yourself attempting to pair your iOS mobile device with a gateway that it has already been paired with. This could happen because the gateway has changed locations, or because you have altered the general configuration of your Monitron site. In that case, tell your iOS device to "forget" its Bluetooth connection with the gateway. Topics • To unpair a gateway from your device To unpair a gateway from your device 1. On your iOS device, choose Settings. 2. On your Settings screen, choose Bluetooth. 3. On the Bluetooth screen, choose the information icon next to the name of your Monitron Gateway. 4. On the next screen, choose Forget This Device. Resetting the Wi-Fi gateway to factory settings If you reuse a gateway that was deleted from Amazon Monitron, you use the commissioning button to reset the gateway to factory settings. This prepares the gateway to be used again for Amazon Monitron. If you delete a gateway that is currently offline, your must perform a factory reset of the device before commissioning it again. Topics • To reset a gateway to factory settings To reset a gateway to factory settings 1. Unplug the gateway. 2. Hold down the commissioning button. 3. Plug the gateway back in. Troubleshooting Bluetooth pairing 147 Amazon Monitron User Guide 4. When the LED lights start slowly blinking, alternating orange and blue, release the commissioning button. 5. Unplug the gateway, wait 10 seconds, and then plug it back in. The gateway is reset. Viewing the list of gateways This page describes how to list your Wi-Fi gateaways in the web or mobile |
AmazonMonitronUG-031 | AmazonMonitronUG.pdf | 31 | reset of the device before commissioning it again. Topics • To reset a gateway to factory settings To reset a gateway to factory settings 1. Unplug the gateway. 2. Hold down the commissioning button. 3. Plug the gateway back in. Troubleshooting Bluetooth pairing 147 Amazon Monitron User Guide 4. When the LED lights start slowly blinking, alternating orange and blue, release the commissioning button. 5. Unplug the gateway, wait 10 seconds, and then plug it back in. The gateway is reset. Viewing the list of gateways This page describes how to list your Wi-Fi gateaways in the web or mobile app. Topics • To list your gateways list using the mobile app • To list your gateways using the web app To list your gateways list using the mobile app 1. Use your smartphone to log in to the Amazon Monitron mobile app. 2. Choose the menu icon in the upper left of the screen. 3. Choose Gateways. A list of all gateways associated with the project is displayed. Viewing the list of gateways 148 Amazon Monitron User Guide To list your gateways using the web app 1. Choose Gateways from the left nav. 2. The gateway list appears in the right pane. Viewing the list of gateways 149 Amazon Monitron User Guide Viewing Wi-Fi gateway details You can view gateway details on your mobile or web app. The following gateway details are viewable: • IP address • Firmware version • Last time commissioned Note You can also view and copy gateway MAC addresses. See Retrieving MAC address details. You can view sensor details on both the mobile and web app. The following section shows you how. Topics • To view Wi-Fi gateway details in the mobile app • To view Wi-FI gateway details in the web app To view Wi-Fi gateway details in the mobile app 1. From the Gateways list, choose the gateway whose details you want to view. Viewing Wi-Fi gateway details 150 Amazon Monitron User Guide 2. From the options box that pops open, select View gateway details. Viewing Wi-Fi gateway details 151 Amazon Monitron User Guide 3. The Gateway details page is displayed. Viewing Wi-Fi gateway details 152 Amazon Monitron User Guide To view Wi-FI gateway details in the web app 1. From the Gateways list, choose the gateway whose details you want to view. Viewing Wi-Fi gateway details 153 Amazon Monitron User Guide 2. The Gateway details page is displayed. Editing Wi-Fi gateway name You can change the display name for your Wi-Fi gateway to find it faster. To edit a gateway name, open your web or mobile app and do the following. Topics • To edit Wi-Fi gateway name To edit Wi-Fi gateway name 1. Select the gateway name you want to edit from the Gateways page. Editing Wi-Fi gateway name 154 Amazon Monitron User Guide Web app view Mobile app view 2. A pop-up will appear prompting you to add a customized name for the gateway. Editing Wi-Fi gateway name 155 Amazon Monitron User Guide Web app view Mobile app view 3. Enter the new name for the gateway and choose Save. Editing Wi-Fi gateway name 156 Amazon Monitron User Guide Web app view Mobile app view 4. You will see a success message confirming the new gateway name. Editing Wi-Fi gateway name 157 Amazon Monitron User Guide Web app view Mobile app view Deleting a Wi-Fi gateway Sensors need a gateway to relay their data to the AWS Cloud. Deleting a gateway might cause some sensors to lose their connection. Exercise caution before deleting a gateway. Deleting a Wi-Fi gateway 158 Amazon Monitron User Guide When you delete a gateway, sensors switch their connection to another gateway that is within range, if there is one. Data transmission from the sensor continues uninterrupted. If no gateway is within range, data transmission is interrupted and the data might be lost. Topics • To delete a gateway using the mobile app • To delete a gateway using the web app To delete a gateway using the mobile app 1. Navigate to the Gateways page. 2. Choose the vertical ellipses icon ( next to the gateway that you want to delete. 3. Choose Delete gateway. 4. Choose Delete again. To delete a gateway using the web app ) 1. Navigate to the the section called “Viewing the list of gateways”. 2. Select the gateway from the table. 3. Choose Delete gateway. Retrieving MAC address details To retrieve your Amazon Monitron gateway's Media Access Control (MAC) address, you can scan the QR code on the gateway device with your mobile phone. Amazon Monitron returns both the MAC address and gateway ID when you scan the QR code. If you are an IT admin, you can use the scanned MAC address to ensure gateway devices are configured |
AmazonMonitronUG-032 | AmazonMonitronUG.pdf | 32 | 4. Choose Delete again. To delete a gateway using the web app ) 1. Navigate to the the section called “Viewing the list of gateways”. 2. Select the gateway from the table. 3. Choose Delete gateway. Retrieving MAC address details To retrieve your Amazon Monitron gateway's Media Access Control (MAC) address, you can scan the QR code on the gateway device with your mobile phone. Amazon Monitron returns both the MAC address and gateway ID when you scan the QR code. If you are an IT admin, you can use the scanned MAC address to ensure gateway devices are configured with the correct network settings before they are commissioned. If you are a technician commissioning gateways, you can use the scanned MAC address to troubleshoot any networking issues with your IT admin. Retrieving MAC address details 159 Amazon Monitron Note User Guide Retrieving MAC addresses by scanning QR codes is only supported for the Amazon Monitron mobile app. The following procedure shows you how to retrieve your gateway device's MAC address. 1. Navigate to the Gateways page. 2. Select the scan icon. 3. Amazon Monitron will display a message explaining what scanning a QR code will do. Select Continue. Retrieving MAC address details 160 Amazon Monitron User Guide 4. On the Scan QR Code page, scan the gateway QR code using your mobile phone camera. When the scan successfully completes, Amazon Monitron displays the Gateway ID and MAC address on the Scan QR Code page in the mobile app. Retrieving MAC address details 161 Amazon Monitron User Guide You can also select the copy icon ( to copy the MAC address. Note ) If not already enabled, Amazon Monitron may need permissions to access your camera to scan the QR code. These permissions must be enabled from the settings page Retrieving MAC address details 162 Amazon Monitron User Guide of your mobile device before you can successfully scan a device QR code. Amazon Monitron will prompt you to enable camera access during the scanning process if permissions haven't already been granted. On Android devices On iOS devices Retrieving MAC address details 163 Amazon Monitron User Guide Retrieving MAC address details 164 Amazon Monitron Assets User Guide Assets, in Amazon Monitron, are the pieces of equipment on your factory floor. Typically, assets are individual machines, but they can also be sections of a larger piece of equipment, part of an industrial process, or any element of your manufacturing model. Amazon Monitron currently supports the following default ISO 20186 standard based machine classes: • Class I – Individual parts of engines and machines, integrally connected to the complete machine in its normal operating condition, for example, production electrical motors of up to 15 kW. • Class II – Medium-sized machines (typically electrical motors with 15 kW to 75 kW output) without special foundations, rigidly mounted engines or machines (up to 300 kW) on special foundations. • Class III – Large prime-movers and other large machines with rotating masses mounted on rigid and heavy foundations that are relatively stiff in the direction of vibration. • Class IV – Large prime-movers and other large machines with rotating masses mounted on rigid and heavy foundations that are relatively soft in the direction of vibration measurement, for example, turbo-generator sets and gas turbines with outputs greater than 10 MW. You can also create custom classes for your assets to fit your use case better. For more information, see Creating custom classes. An asset is also the basis for viewing the health of your machines. To monitor machine activity, you pair one or more sensors to the asset that you want to monitor. Each sensor gives you insight into how that part of the asset is functioning, and together they provide an overview of the entire asset. You can assign each sensor positioned on an asset can its own machine class. The following diagram shows one asset, an electric motor pump set. It has four positions, each with a sensor, two on the motor and two on the pump. Each sensor collects data on the temperature and vibration levels of that specific position on the pump. Amazon Monitron then analyzes that data by comparing it to the baseline temperature and vibration levels of that position to determine when a change, or abnormality, occurs. When that happens, it sends a notification on the Amazon Monitron app. 165 Amazon Monitron User Guide This chapter explains how to manage your assets with Amazon Monitron, and how to pair them to the sensors that monitor their health. Topics • Creating asset classes • Managing assets • Viewing the list of assets • Adding an asset • Changing an asset name • Moving an asset • Deleting an asset Creating asset classes Amazon Monitron offers four default machine classes based on ISO |
AmazonMonitronUG-033 | AmazonMonitronUG.pdf | 33 | baseline temperature and vibration levels of that position to determine when a change, or abnormality, occurs. When that happens, it sends a notification on the Amazon Monitron app. 165 Amazon Monitron User Guide This chapter explains how to manage your assets with Amazon Monitron, and how to pair them to the sensors that monitor their health. Topics • Creating asset classes • Managing assets • Viewing the list of assets • Adding an asset • Changing an asset name • Moving an asset • Deleting an asset Creating asset classes Amazon Monitron offers four default machine classes based on ISO 20816 Standards. When you add an asset position, you can choose any of these four default classes as the machine class to use for detecting anomalies with your assets. Amazon Monitron then uses the assigned asset class to generate warnings and alarms on asset condition. If your asset types don't align with the default machine classes offered by Amazon Monitron, you can create custom machine classes for your assets. Once created, these custom classes are available to be assigned to all asset positions in a project. Creating asset classes 166 Amazon Monitron Important User Guide Custom classes can only be created using the Amazon Monitron web app. Only the Amazon Monitron project admin can create, update, and delete custom asset classes. Topics • Creating a custom class • Updating a custom class • Deleting a custom class Creating a custom class To create a custom class 1. Open the Amazon Monitron console at https://console.aws.amazon.com/monitron. 2. Choose Create project. 3. If you're creating a project for the first time, follow the steps outlined in Creating a project. If you're choosing an existing project, from the left navigation menu, select Projects, and then select the project you want to create custom classes for. 4. From the project details page, choose Open in Amazon Monitron web app. Creating a custom class 167 Amazon Monitron User Guide 5. In the Amazon Monitron web app page, from the left navigation pane, choose Settings. Creating a custom class 168 Amazon Monitron User Guide 6. Then, select from Classes, select Create class. Creating a custom class 169 Amazon Monitron User Guide 7. On the Create custom class page, do the following: • In Class details, for Class name – A name for your custom class. • Description – A description for your custom machine class. • In Measurement details, for Measurement thresholds – Custom measurement thresholds for your assets. 8. Choose Save. Creating a custom class 170 Amazon Monitron User Guide Updating a custom class To update a custom class 1. Open the Amazon Monitron console at https://console.aws.amazon.com/monitron. 2. Choose Create project. 3. If you're creating a project for the first time, follow the steps outlined in Creating a project. If you're choosing an existing project, from the left navigation menu, select Projects, and then select the project you want to create custom classes for. 4. From the project details page, choose Open in Amazon Monitron web app. 5. In the Amazon Monitron web app page, from the left navigation pane, choose Settings. Updating a custom class 171 Amazon Monitron User Guide 6. Then, from Classes, select the class you would like to update, and select Edit. Updating a custom class 172 Amazon Monitron User Guide 7. On the Edit class page, do the following: Updating a custom class 173 Amazon Monitron User Guide • In Class details, for Class name – A name for your custom class. • Description – A description for your custom machine class. • In Measurement details, for Measurement thresholds – Custom measurement thresholds for your assets. 8. Choose Save. Note The edited machine class will go into effect during the next Amazon Monitron measurement interval. Deleting a custom class To delete a custom class 1. Open the Amazon Monitron console at https://console.aws.amazon.com/monitron. 2. Choose Create project. 3. If you're creating a project for the first time, follow the steps outlined in Creating a project. If you're choosing an existing project, from the left navigation menu, select Projects, and then select the project you want to create custom classes for. 4. From the project details page, choose Open in Amazon Monitron web app. Deleting a custom class 174 Amazon Monitron User Guide 5. In the Amazon Monitron web app page, from the left navigation pane, choose Settings. Deleting a custom class 175 Amazon Monitron User Guide 6. Then, from Classes, select the machine class you would like to delete, and select Delete. Deleting a custom class 176 Amazon Monitron User Guide Important You can't delete custom machine classes that are currently in use by one or more positions. You will be prompted with a list of positions currently using the machine class and you will need to update these positions to a |
AmazonMonitronUG-034 | AmazonMonitronUG.pdf | 34 | app. Deleting a custom class 174 Amazon Monitron User Guide 5. In the Amazon Monitron web app page, from the left navigation pane, choose Settings. Deleting a custom class 175 Amazon Monitron User Guide 6. Then, from Classes, select the machine class you would like to delete, and select Delete. Deleting a custom class 176 Amazon Monitron User Guide Important You can't delete custom machine classes that are currently in use by one or more positions. You will be prompted with a list of positions currently using the machine class and you will need to update these positions to a different machine class before deleting the machine class attached to these positions. 7. To confirm deletion, type delete, and then select Save. Deleting a custom class 177 Amazon Monitron Managing assets User Guide Use the Amazon Monitron app to list all the assets in your site or project. Managing assets 178 Amazon Monitron User Guide Managing assets 179 Amazon Monitron User Guide Viewing the list of assets The Assets page displays the list of assets. The Assets page is the app's main page. The main page is the page you see when you open the app. To return to the Assets page from another page in the app, use this procedure. Topics • To open the Assets list To open the Assets list 1. Choose the menu icon (☰). 2. Choose Assets. Adding an asset After you set up your site or project, add the assets that your sensors will monitor. Note After you create an asset, you can change only its name. Topics • Adding assets using the mobile app • Adding assets using the web app Adding assets using the mobile app To add an asset using the mobile app 1. Sign in to your mobile app and select the project you want to add an asset to. Viewing the list of assets 180 Amazon Monitron User Guide Adding assets using the mobile app 181 Amazon Monitron User Guide 2. Make sure you're on the correct site your project that you want to add the asset to. The project or site name indicates that you are at that level in the app. For more information about changing from site level to project level and vice versa, see Navigating between projects and sites in the mobile app. 3. From the Assets page, choose Add asset. 4. On the Add asset page, for Asset name, add a name for the asset you want to create and then select Add. Adding assets using the mobile app 182 Amazon Monitron User Guide Adding assets using the mobile app 183 Amazon Monitron Note User Guide If you have a QR code identifying the asset name, you can scan it by selecting the QR code. When you've added your first asset, it's displayed on the Assets list page. Adding assets using the web app To add an asset using the web app 1. Sign in to your web app and select the project you want to add an asset to. 2. From the left navigation menu, choose Sites, and then select the site you want to the asset to. Adding assets using the web app 184 Amazon Monitron Note You can also add the asset directly to a project. 3. From the Assets page, choose Add asset. User Guide 4. On the Add asset page, for Asset name, add a name for the asset you want to create and then select Add asset. When you've added your first asset, it's displayed on the Assets list page. Adding assets using the web app 185 Amazon Monitron User Guide Changing an asset name After you add an asset, you can change both its name and machine class. Topics • To change an asset's name in the mobile app • To change an asset's name in the web app To change an asset's name in the mobile app 1. 2. From the app's main menu, choose Assets. For Asset details, choose Actions. 3. Choose Edit asset. 4. Enter a new name. 5. Choose Save. To change an asset's name in the web app 1. 2. Select the asset. In the large tab, choose the Actions button from the right end of the row containing the asset name. Changing an asset name 186 Amazon Monitron User Guide 3. Enter a new name. 4. Choose Save. Moving an asset Assets in a project can be grouped under various sites. If you need to re-organize your assets and sites, you can choose to move an asset from one site to another without having to create each asset again. Note You can move assets from the project level to the site level. However, you can't move assets from the site level to the project level. Once an asset is moved, it |
AmazonMonitronUG-035 | AmazonMonitronUG.pdf | 35 | the right end of the row containing the asset name. Changing an asset name 186 Amazon Monitron User Guide 3. Enter a new name. 4. Choose Save. Moving an asset Assets in a project can be grouped under various sites. If you need to re-organize your assets and sites, you can choose to move an asset from one site to another without having to create each asset again. Note You can move assets from the project level to the site level. However, you can't move assets from the site level to the project level. Once an asset is moved, it continues generating notifications in its new destination site. All positions associated with the asset move to the new site. However, it stops generating notifications and being visible to users in its older source site. Moving an asset 187 Amazon Monitron Important User Guide Only an user with admin access to both source and destination sites can move an asset. Topics • To move an asset on the web app • To move an asset on the mobile app To move an asset on the web app 1. From the web app's main menu, choose Assets. 2. Choose the asset that you want to move. 3. From the asset menu, choose Actions, and then choose Move asset. 4. From the dialog box that opens, select a site to move your asset to from the New site dropdown menu, and then select Move. To move an asset on the web app 188 Amazon Monitron User Guide The app displays a success message if your asset is moved successfully. To move an asset on the web app 189 Amazon Monitron User Guide To move an asset on the mobile app 1. From the mobile app's main menu, choose Assets. 2. Choose asset that you want to move to a new site. Then, open the asset details menu. To move an asset on the mobile app 190 Amazon Monitron User Guide To move an asset on the mobile app 191 Amazon Monitron User Guide 3. From the asset details menu, choose Move asset. To move an asset on the mobile app 192 Amazon Monitron User Guide To move an asset on the mobile app 193 Amazon Monitron User Guide 4. From the asset page, from New site, choose the new site you want to move the asset to. Then, choose Move. To move an asset on the mobile app 194 Amazon Monitron User Guide To move an asset on the mobile app 195 Amazon Monitron User Guide The app displays a success message if your asset is moved successfully. Deleting an asset Deleting an asset removes all associated sensors and their positions, in addition to any historical data associated with them. Topics • To delete an asset To delete an asset 1. From the app's main menu, choose Assets. 2. Choose the asset that you want to delete. 3. For Asset details, choose Actions. 4. Choose Delete asset. 5. Choose one of the following options. • If there are no sensors paired with the asset, choose Delete and go to the next step. Deleting an asset 196 Amazon Monitron User Guide • If there are sensors paired with the asset, delete them. Choose Delete sensors and positions. When you delete a sensor or position, all historical measurements taken at this position will also be deleted. It can take some time for Amazon Monitron to delete all the paired sensors and positions. 6. Choose Delete. To delete an asset 197 Amazon Monitron Sensors User Guide Sensors collect the data from your equipment, then Amazon Monitron uses that data to detect developing abnormalities. Where you mount a sensor (the position) is extremely important for collecting and analyzing data. To get a more detailed picture of your asset's health, you might need to collect data from multiple positions on your asset. You can place sensors on up to 20 positions on each asset. Each sensor position can be assigned a different machine class. If you have complex machinery with more than one potential point of failure, we recommend that you collect data from multiple positions. Topics • Positioning a sensor • Mounting a sensor • Adding a sensor position • Pairing a sensor to an asset • Renaming a sensor position • Editing machine class • Deleting a sensor • Deleting a sensor position • Understanding sensor details • Identifying sensor position • Ex-rated sensors Positioning a sensor To detect abnormalities in machine components, mount sensors in all locations where temperature and vibrations can be measured effectively. To achieve the greatest accuracy: • Mount the sensor directly onto the housing of the target component. • Minimize the length of the vibration transmission path (the distance between the source of vibration and sensor). Positioning a sensor 198 Amazon |
AmazonMonitronUG-036 | AmazonMonitronUG.pdf | 36 | sensor position • Pairing a sensor to an asset • Renaming a sensor position • Editing machine class • Deleting a sensor • Deleting a sensor position • Understanding sensor details • Identifying sensor position • Ex-rated sensors Positioning a sensor To detect abnormalities in machine components, mount sensors in all locations where temperature and vibrations can be measured effectively. To achieve the greatest accuracy: • Mount the sensor directly onto the housing of the target component. • Minimize the length of the vibration transmission path (the distance between the source of vibration and sensor). Positioning a sensor 198 Amazon Monitron User Guide • Avoid mounting the sensor where its measurements may oscillate due to natural frequencies, such as on sheet metal covers. Vibration will attenuate up to 30-36" (75–90 cm) from the source. Attributes of the vibration transmission path that can reduce the transmission path length include: • the number of mounting surfaces, which can cause signal reflection • materials such as rubber or plastic, which can absorb vibration Note Amazon Monitron sensors are 3-axis vibration sensors. The X, Y, and Z marks indicate the directions of the 3 three axes. These axes are marked on the sensor body. Therefore, it is not necessary to align any particular axis with the direction of the asset's vibration. The following example of an electric motor pump set shows sensor locations, with four positions: two on the motor and two on the pump. The following example shows where you might mount sensors if your primary concern is the motor rather than the pump. Positioning a sensor 199 Amazon Monitron User Guide Gearboxes and bearings are also examples of common locations where you might want to place sensors. For complex equipment with multiple moving parts (such as gearboxes), position the sensor to minimize the length of the transmission path from the primary vibration source. Note that vibration is reduced when it is transmitted between adjacent parts of equipment, so the shortest distance between the sensor and the source of vibration is not always the best option. The following example of a gearbox shows how vibration can be transmitted through equipment in this way, along with a potential location for a sensor to detect this vibration. Positioning a sensor 200 Amazon Monitron User Guide For other types of equipment, the best position can be less obvious. For example, when placing a sensor to monitor bearings, position it close to the bearing's load zone, which is based on the direction of the load on the bearings as shown below. Different types of loads on the bearings result in different load zones. Placing the sensor as close as possible to the center of the load zone is most likely to provide the best data. Positioning a sensor 201 Amazon Monitron User Guide For information about how to mount sensors, see Mounting a sensor. Mounting a sensor Warning Before you install and use sensors, see the Amazon Monitron Sensor Device Safety and Compliance Guide. Before you install and use Ex-rated sensors, see the Ex Safety and Compliance Guide for all warnings and instructions. The temperature and vibration detectors are located on the base of the Amazon Monitron sensors. Any area of the base is effective as a target contact area, but the contact area must be at least 30 x 25 mm for reliable detection. Center the target contact area over the mounting location for the most reliable results. The circular aluminum sensor (in the center of the target contact area) conducts heat directly from the asset's surface to the temperate sensing mechanism inside the Amazon Monitron sensor. Mounting a sensor 202 Amazon Monitron User Guide Determine the place and orientation where you can most effectively monitor the asset, and then mount sensor at that spot. To mount the sensor, you need to purchase an industrial adhesive. We recommend using cyanoacrylate epoxies like Loctite 454 and Loctite 3090 or Loctite 4070 or something similar. If the surface on which you mount the sensor is flat and relatively smooth, only a thin layer of adhesive such as Loctite 454 is needed. If the surface is rounded or somewhat uneven, apply a slightly thicker layer of adhesive such as Loctite 3090 or Loctite 4070. If you are uncertain of where to mount your sensor, see Positioning a sensor. Warning When installing sensors, check and obey applicable safety regulations. You are solely responsible for safely installing the sensor on any equipment or machine part. To mount a sensor, you use industrial adhesive. Always consult and obey the adhesive manufacturer's safety and handling instructions. For more information about the recommended adhesive, see Loctite 454 Technical Information, or Loctite 3090 Technical Information, or Loctite 4070 Technical Information, as appropriate. To mount a sensor 1. Remove all oil and grease from the position |
AmazonMonitronUG-037 | AmazonMonitronUG.pdf | 37 | Loctite 3090 or Loctite 4070. If you are uncertain of where to mount your sensor, see Positioning a sensor. Warning When installing sensors, check and obey applicable safety regulations. You are solely responsible for safely installing the sensor on any equipment or machine part. To mount a sensor, you use industrial adhesive. Always consult and obey the adhesive manufacturer's safety and handling instructions. For more information about the recommended adhesive, see Loctite 454 Technical Information, or Loctite 3090 Technical Information, or Loctite 4070 Technical Information, as appropriate. To mount a sensor 1. Remove all oil and grease from the position on the asset where you want to mount the sensor. 2. If the surface that you're mounting the sensor to is flat and relatively smooth, apply a thin layer of adhesive such as Loctite 454 to the bottom of the sensor, maximizing the area that will be in contact with the asset. Mounting a sensor 203 Amazon Monitron User Guide If the surface is rounded or somewhat uneven, apply a slightly more liberal layer of adhesive such as Loctite 3090 or Loctite 4070 to the bottom of the sensor. The layer of adhesive can bridge distances of up to 5 mm between the surface and the sensor if necessary. 3. Hold the sensor to the mounting location on the machine part for 30 seconds, pressing firmly. If you're mounting the sensor on a curved surface, put a small amount of additional adhesive on each side for better contact between the sensor and the surface. Based on the surface and the adhesive used, your results should look similar to the following. Adding a sensor position When you pair a sensor to an asset, you record the type of position. The type of position tells Amazon Monitron how to assess the position when it analyzes the data from that sensor. You can create and update asset positions from both the Amazon Monitron web app and the Amazon Monitron mobile app. Using the apps, you can: • Add a new position to an existing asset • Add a new position to new asset • Pair a new sensor with an existing position • Add a new position to an existing asset without assigned position Topics Adding a sensor position 204 Amazon Monitron User Guide • To add a sensor position on the web app • To add a sensor position on the mobile app To add a sensor position on the web app 1. Choose the sensor whose position you want to create or edit from the Assets list. 2. Select the Add position button. 3. In the dialog box that opens, enter your Position name, Position type and Machine class. To add a sensor position on the web app 205 Amazon Monitron User Guide 4. Choose Save. 5. Your position is added to the asset. To add a sensor position on the mobile app 1. Choose the sensor whose position you want to create or edit from the Assets list. To add a sensor position on the mobile app 206 Amazon Monitron User Guide 2. Select the Add position button. To add a sensor position on the mobile app 207 Amazon Monitron User Guide To add a sensor position on the mobile app 208 Amazon Monitron User Guide 3. In the dialog box that opens, enter your Posion name, Position type, and Machine class. 4. Choose Next. 5. Re-scan your sensor with your mobile device to save the position. To add a sensor position on the mobile app 209 Amazon Monitron User Guide To add a sensor position on the mobile app 210 Amazon Monitron User Guide 6. Your position is added to the asset. To add a sensor position on the mobile app 211 Amazon Monitron User Guide Pairing a sensor to an asset After you've added an asset, pair it to one or more of the sensors to monitor its health. Each sensor is mounted on the asset in its own position. Each sensor mounted on the asset can be assigned its own machine class. When you pair a sensor to an asset, you record the type of position. The type of position tells Amazon Monitron how to assess the position when it analyzes the data from that sensor. Each position can give a very different view of the asset. You will often need to monitor multiple locations on a large asset to get a clear picture of its health. You can place up to 20 sensors at different positions on an asset. Less complex assets might require only one or two sensors. Each sensor measures the temperature and vibration at its position. You can name a position anything you like, and you can change the name later if necessary. For example, a sensor set up to monitor the pump |
AmazonMonitronUG-038 | AmazonMonitronUG.pdf | 38 | position when it analyzes the data from that sensor. Each position can give a very different view of the asset. You will often need to monitor multiple locations on a large asset to get a clear picture of its health. You can place up to 20 sensors at different positions on an asset. Less complex assets might require only one or two sensors. Each sensor measures the temperature and vibration at its position. You can name a position anything you like, and you can change the name later if necessary. For example, a sensor set up to monitor the pump in the previous example might have a position of Left Position, with a position type of Pump. The position name identifies the location, whereas the position type tells Amazon Monitron which part of the asset it's monitoring. You can also edit the machine class assigned to each sensor. For more information about where to place sensors, see Positioning a sensor. Important After you pair a sensor to an asset, Amazon Monitron establishes a baseline for that position. The baseline tells Amazon Monitron how the asset performs under normal conditions. Amazon Monitron uses this information to identify abnormal conditions. During this time, Amazon Monitron assumes that conditions are normal and won't produce any alarms. Topics • To pair a sensor to an asset To pair a sensor to an asset 1. Ensure that near field communication (NFC) is turned on for your smartphone. Pairing a sensor to an asset 212 Amazon Monitron Tip User Guide For many smartphone models, NFC is turned on by default. The following resources might help you determine whether you need to turn on NFC, and how to do so: • About NFC (Samsung) • Models that support NFC Tag Reader (iPhone) 2. From the Assets list, choose the asset. • If you just created the asset: Choose Add position. • If you created the asset earlier, and have already paired more than one sensor to it: a. After you choose the asset, you will see a dropdown list of sensors associated with that asset. Choose the View option at the top of that list. To pair a sensor to an asset 213 Amazon Monitron User Guide b. Choose Pair sensor. To pair a sensor to an asset 214 Amazon Monitron User Guide 3. Place your sensor on the machine in the correct location. For more information about placing sensors, see Positioning a sensor and Mounting a sensor. 4. Name the position that the sensor will monitor. We recommend that you use a name that is clear and easy for you to work with. 5. For Position type, choose the position type. Valid values: • Bearing • Compressor • Fan • Gearbox To pair a sensor to an asset 215 Amazon Monitron • Motor • Pump • Other Note User Guide After you pair a sensor to an asset, you can't change the position type. If you need to change the type, you must delete the sensor and re-add it. 6. For Machine class, choose the machine class of the asset part you are positioning the sensor on. Valid options are based on the ISO 20816 standards. Class I Individual parts of engines and machines, integrally connected to the complete machine in its normal operating condition, for example, production electrical motors of up to 15 kilowatts (kW) or 20 horsepower (hp). Class II Medium-sized machines (typically electrical motors with 15 to 75 kW (20 to 101 hp) output) without special foundations, rigidly mounted engines or machines (up to 300 kW or 402 hp) on special foundations. Class III Large prime-movers and other large machines with rotating masses mounted on rigid and heavy foundations that are relatively stiff in the direction of vibration. Class IV Large prime-movers and other large machines with rotating masses mounted on rigid and heavy foundations that are relatively soft in the direction of vibration measurement, for example, turbo-generator sets and gas turbines with outputs greater than 10 megawatt (MW) or 13,404 hp. 7. Choose Next. 8. Hold your smartphone close to the sensor to commission it. Don't move your smartphone while you are commissioning the sensor. To pair a sensor to an asset 216 Amazon Monitron User Guide It can take a few moments for Amazon Monitron to commission the sensor and pair with it. While it's connecting, you will see the following message. Note The appropriate way to hold your mobile device while pairing depends on the type of mobile device you have. For more information, see Troubleshooting Amazon Monitron device issues. To pair a sensor to an asset 217 Amazon Monitron User Guide When more than one sensor is paired with a given asset, the Assets page shows each sensor position and its health status, but not the specific details about |
AmazonMonitronUG-039 | AmazonMonitronUG.pdf | 39 | 216 Amazon Monitron User Guide It can take a few moments for Amazon Monitron to commission the sensor and pair with it. While it's connecting, you will see the following message. Note The appropriate way to hold your mobile device while pairing depends on the type of mobile device you have. For more information, see Troubleshooting Amazon Monitron device issues. To pair a sensor to an asset 217 Amazon Monitron User Guide When more than one sensor is paired with a given asset, the Assets page shows each sensor position and its health status, but not the specific details about each position. To display the details, choose the position from the list. For more information about the data you can monitor with each asset, see Understanding sensor measurements. Positions are displayed in status order. For example, a position that's in an alarm state is displayed above a position that's in an acknowledged state. Positions that are in a healthy state follow those in an acknowledged state. Renaming a sensor position Topics • Renaming a sensor position on the mobile app Renaming a sensor position 218 Amazon Monitron User Guide • Renaming a sensor position on the web app Renaming a sensor position on the mobile app 1. From the Assets list, choose the asset with the sensor position whose name you want to change. 2. Choose the sensor with the position whose name you want to change. 3. Choose the Sensor details tab. 4. Under Position details, choose Actions. 5. Choose Edit position details. 6. For Position name, enter a new name. 7. Choose Save. Renaming a sensor position on the web app 1. Select the position. Choose the Actions button in the Positions table. Renaming a sensor position on the mobile app 219 Amazon Monitron User Guide 2. Choose Edit position name. 3. For Position name, enter a new name. 4. Choose Save. Editing machine class You can edit the machine class of a sensor from both the mobile and web apps, from either the Asset detail section or the Position detail section. When you edit a sensor's machine class, asset condition alerts based on the updated machine class take effect from the next measurement after the update. Important You cannot edit a sensor's machine class if it has an unresolved alert. You must resolve any alerts before editing machine class. Topics • To edit machine class on the mobile app • To edit machine class on the web app Editing machine class 220 Amazon Monitron User Guide • To edit machine class from the position detail page To edit machine class on the mobile app 1. 2. From the Assets list, choose the asset with the sensor position you want to edit. From the Positions list, choose the sensor with the position whose machine class you want to change. 3. Choose to see more sensor details. To edit machine class on the mobile app 221 Amazon Monitron User Guide 4. From the options that appear, choose Edit machine class. To edit machine class on the mobile app 222 Amazon Monitron User Guide To edit machine class on the mobile app 223 Amazon Monitron User Guide 5. From Edit machine class choose the new machine class you want to assign to the sensor. Select Save. Note The new machine class will take effect at the next measurement interval. The single- axis chart threshold will be updated. To edit a machine class from the position detail page 1. From the Position details list, choose the Actions tab. To edit machine class on the mobile app 224 Amazon Monitron User Guide 2. From the options that appear, choose Edit machine class. To edit machine class on the mobile app 225 Amazon Monitron User Guide To edit machine class on the mobile app 226 Amazon Monitron User Guide 3. From the Edit machine class menu choose the new machine class you want to assign to the sensor. Choose Next. Note The new machine class will take effect at the next measurement interval. The single- axis chart threshold will be updated. To edit machine class on the web app 1. 2. From the Assets table, choose the Actions button. From the options, choose Edit machine class. 3. From the Edit machine class menu choose the new machine class you want to assign to the sensor and then select Save changes. Note The new machine class will take effect at the next measurement interval and impact position status. The single-axis chart threshold will be updated. To edit machine class on the web app 227 Amazon Monitron User Guide To edit machine class from the position detail page 1. 2. From the Positions table, choose the Actions button. From the options, choose Edit machine class. 3. From the Edit machine class menu choose the new |
AmazonMonitronUG-040 | AmazonMonitronUG.pdf | 40 | Edit machine class. 3. From the Edit machine class menu choose the new machine class you want to assign to the sensor and then select Save changes. Note The new machine class will take effect at the next measurement interval and impact position status. The single-axis chart threshold will be updated. To edit machine class on the web app 227 Amazon Monitron User Guide To edit machine class from the position detail page 1. 2. From the Positions table, choose the Actions button. From the options, choose Edit machine class. 3. From the Edit machine class menu choose the new machine class you want to assign to the sensor and then select Save changes. Note The new machine class will take effect at the next measurement interval. The single- axis chart threshold will be updated. Deleting a sensor Deleting a sensor prevents Amazon Monitron from collecting more data with it. It doesn't delete the data that it has already collected. Topics To edit machine class from the position detail page 228 Amazon Monitron User Guide • To delete a sensor in the mobile app • To delete a sensor in the web app To delete a sensor in the mobile app 1. From the Assets list, choose the asset that is paired to the sensor that you want to delete. 2. Choose the sensor. 3. Under Sensor, choose Actions. 4. Choose Delete sensor. 5. Choose Delete. After a sensor has been deleted, the status for that position says No sensor. To delete a sensor in the mobile app 229 Amazon Monitron User Guide To delete a sensor in the web app • Choose Delete from the Sensor details tab. To delete a sensor in the web app 230 Amazon Monitron User Guide Deleting a sensor position Deleting a sensor position removes that data collection point from the asset. If a sensor is still paired to this position, you need to remove it before you can delete the position. Topics • To delete a sensor position in the mobile app • To delete a sensor position in the web app To delete a sensor position in the mobile app 1. From the Assets list, choose the asset that has the sensor position that you want to delete. 2. Under Sensors, choose Actions. 3. Choose Delete position. 4. If the position has a sensor paired to it, delete the sensor by choosing Delete sensor. Otherwise, skip to the next step. Deleting a sensor position 231 Amazon Monitron User Guide 5. Choose Delete. To delete a sensor position in the web app 1. Select the position. 2. Choose the Actions button in the Positions table. 3. Choose Delete position. 4. If the position has a sensor paired to it, delete the sensor by choosing Delete sensor. Otherwise, skip to the next step. To delete a sensor position in the web app 232 Amazon Monitron User Guide 5. Choose Delete. Understanding sensor details To check that a sensor is performing as expected, check its details page. The Sensor details page shows the following information: • Sensor ID • Sensor status • Date the sensor was last commissioned • Date of the last measurement • Last gateway it connected to • Current signal strength of the last gateway • Sensor type • Firmware version • Sensor battery status Topics • Viewing sensor details • Sensor connectivity status Understanding sensor details 233 Amazon Monitron • Sensor battery status Viewing sensor details User Guide You can view sensor details on both the mobile and web app. The following section shows you how. To view sensor details in the mobile app 1. From the Assets list, choose the asset that is paired with the sensor that you want to view. 2. Choose the sensor. 3. Select the Position that is connected to the sensor you want to view. 4. Choose the Sensor details tab. 5. Choose the Sensor Actions button. 6. Choose View sensor details. Viewing sensor details 234 Amazon Monitron User Guide The Sensor details page is displayed. To view sensor details in the web app 1. From the Assets list, choose the asset that is paired with the sensor that you want to view. Viewing sensor details 235 Amazon Monitron User Guide 2. Information about the sensor will be shown automatically in the Sensor details tab on the lower right side of the app window. Sensor connectivity status When you create a sensor, you can monitor its position and connectivity status on the Amazon Monitron assets list. Sensor position states are healthy/maintenance/warning/alarm and sensor connectivity states are online/offline. A sensor's default state is online. If it times out due to connectivity issues, its state will change to offline. Once connectivity is restored, the sensor will return to an online state. A sensor will maintain its most |
AmazonMonitronUG-041 | AmazonMonitronUG.pdf | 41 | details 235 Amazon Monitron User Guide 2. Information about the sensor will be shown automatically in the Sensor details tab on the lower right side of the app window. Sensor connectivity status When you create a sensor, you can monitor its position and connectivity status on the Amazon Monitron assets list. Sensor position states are healthy/maintenance/warning/alarm and sensor connectivity states are online/offline. A sensor's default state is online. If it times out due to connectivity issues, its state will change to offline. Once connectivity is restored, the sensor will return to an online state. A sensor will maintain its most recent states if it goes offline. An asset's badge on the asset list shows its most severe position and connectivity states. If its position includes both warning and healthy states, it will have a warning state on the asset list. If at least one asset is offline, it will have an offline state in the asset list. Note If a sensor is offline, its status is prioritized in the Amazon Monitron application asset list. The app does not support notifications if a sensor goes offline, but the app will indicate if a device goes offline. The following images show sensors that are offline. Sensor connectivity status 236 Amazon Monitron User Guide Sensor battery status To help you keep track of your sensor health, each Amazon Monitron displays a sensor battery life status. You can check your sensor battery life from both the mobile app and the web app. You can use this battery status to decide when to buy new sensors. Note Estimated remaining battery life is calculated based on 5 years sensor battery life for a sensor taking measurements hourly. Sensor battery status 237 Amazon Monitron Important User Guide Battery life status is not available for sensors with a firmware version less than 1.6.0. You need to wait until the sensor is updated to view battery life status. The following table shows the different sensor battery states: Battery status Condition Time remaining Action Normal Sensor battery is in No sensor battery healthy state. monitoring currently needed. Low Battery has less than Begin monitoring 1 year of life left. your sensor battery. Urgent Battery has less than Replace your sensor 3 months of life left. as soon as possible. Unknown Battery life status is 1. If commissioning unknown. sensor for the first time, wait for a minute till the sensor sends its first measurement. 2. Then, make sure you have commision ed a gateway correctly and take Sensor battery status 238 Amazon Monitron User Guide Battery status Condition Time remaining Action a measurement using the mobile app. See Gateways and Taking a one-time measurement for details. Note If you do not replace your sensor after its battery status is urgent, the sensor's connectivity state will change to Offline. Identifying sensor position Use the mobile app to find sensors in the factory or shop floor without searching through your asset list. Topics • Identifying paired sensor • Missing or unread sensor • Permissions and site commissioning issues • Scanning sensor from another site Identifying paired sensor 1. If the sensor has been paired, select the scan sensor icon from your asset page to scan any sensor affiliated with your project. Identifying sensor position 239 Amazon Monitron User Guide 2. Select a desired asset to scan. 3. Hold your phone near the sensor and scan it to read its position details. It may take a few moments for the mobile app to generate results. Identifying paired sensor 240 Amazon Monitron User Guide 4. After you've scanned your sensor successfully, your mobile app will show the sensor's position and details. Identifying paired sensor 241 Amazon Monitron User Guide Identifying paired sensor 242 Amazon Monitron User Guide Missing or unread sensor If the sensor is not read during the scan, place your phone correctly over the sensor until you see a success message. Missing or unread sensor 243 Amazon Monitron User Guide If no sensor was added, add an asset and try again. Permissions and site commissioning issues If the sensor hasn’t been commissioned for a site, commission the sensor and try again. Permissions and site commissioning issues 244 Amazon Monitron User Guide If the sensor was commissioned for a site that you can't access, update site permissions in the app and try again to read the sensor’s position details. Permissions and site commissioning issues 245 Amazon Monitron User Guide Scanning sensor from another site If you scan a sensor that is commissioned for another site, and you're redirected to that site, scan the sensor on that site. Scanning sensor from another site 246 Amazon Monitron User Guide Ex-rated sensors Warning Before installing and using a sensor, see Ex Safety and Compliance Guide for all warnings and instructions. Amazon Monitron can notify you about |
AmazonMonitronUG-042 | AmazonMonitronUG.pdf | 42 | the sensor was commissioned for a site that you can't access, update site permissions in the app and try again to read the sensor’s position details. Permissions and site commissioning issues 245 Amazon Monitron User Guide Scanning sensor from another site If you scan a sensor that is commissioned for another site, and you're redirected to that site, scan the sensor on that site. Scanning sensor from another site 246 Amazon Monitron User Guide Ex-rated sensors Warning Before installing and using a sensor, see Ex Safety and Compliance Guide for all warnings and instructions. Amazon Monitron can notify you about product issues that could affect safety in explosive and hazardous areas. You’ll receive these notifications in the web app if you’re an existing customer with sensors installed. Ex-rated sensors 247 Amazon Monitron User Guide If a sensor has an urgent safety advisory, you’ll receive a notification and explanation when you log on to the web or mobile app. Before you can proceed, you'll be required to acknowledge the advisory and perform the recommended actions in the safety warning. For example, you may need to physically remove a sensor from a hazardous area, as it could be a potential ignition source. Ex-rated sensors 248 Amazon Monitron User Guide When a sensor has a healthy position status, you can use the sensor to take measurements, view sensor details, or delete the sensor. If you need to delete a sensor, make sure it’s in a healthy state first. A sensor’s position must be in a healthy state before you can delete it. If you do remove a sensor that is under safety notification or not in a healthy state, you’ll receive a notification explaining that you must clear the alert first. To clear the alert: 1. In the asset list, select the unhealthy sensor. 2. Review the errors. 3. 4. Select Acknowledge to confirm that you understand the active alerts related to the sensor. Select Resolve to fix the anomaly that the sensor is reporting. After resolving the issue, the sensor should return to a healthy state. 5. Delete the sensor from either the Asset list or the Position details page. Ex-rated sensors 249 Amazon Monitron User Guide If you try to commission a sensor under a safety notification, the commissioning process will fail. You’ll receive a notification describing the reason for the failure. Ex-rated sensors 250 Amazon Monitron User Guide Understanding sensor measurements and monitoring machine abnormalities Amazon Monitron monitors temperature and vibration data from sensors and watches asset conditions for abnormalities that might indicate developing faults. You monitor your assets either with the Amazon Monitron web app, or with the Amazon Monitron mobile app, which you download and install on your smartphone. Amazon Monitron supports only smartphones using Android 8.0+ or iOS 14+ with Near Field Communication (NFC) and Bluetooth. This topic describes how to read sensor measurements, respond to notifications about machine abnormalities, and take one-time measurements. Topics • Choosing your measurement viewing platform • Viewing sensor measurements • Understanding sensor measurements • Understanding asset status • Acknowledging a machine abnormality • Resolving an abnormality • Taking a one-time measurement Choosing your measurement viewing platform There are two ways to use Amazon Monitron to view your assets' measurements and abnormalities. You can view them in the mobile app, or you can view them in the web app. Each way has its advantages. With the mobile app, you use your phone's Bluetooth and Near Field Communication (NFC) capabilities to install and configure gateways and sensors, as explained in Wi-Fi gateways. With the web app, you download your data to a .csv file. Also, your monitor is probably bigger than your phone, so the web app may be a better place to see measurements using line graphs. You can activate either the mobile app or the web app by clicking on a link to your project. This is the link that the administrator sends to the user, as explained in Sending an email invitation. But Choosing your measurement viewing platform 251 Amazon Monitron User Guide you can re-generate this link from the Projects page by selecting a user and then choosing Email instructions, or by choosing Copy link under Project details. Topics • In-app updates In-app updates For access to the latest Amazon Monitron features, regularly check your mobile device for updates. Periodically, Amazon Monitron releases new application versions that you'll need to manually update if you don't enable automatic updates. These notifications will be provided on the web app as they become available. Flexible and immediate updates Amazon Monitron provides two kinds of in-app updates: flexible and immediate. Flexible updates allow you to elect whether or not to update the Amazon Monitron app once you’ve signed in. Immediate updates contain security updates, and must be installed in order to use the |
AmazonMonitronUG-043 | AmazonMonitronUG.pdf | 43 | • In-app updates In-app updates For access to the latest Amazon Monitron features, regularly check your mobile device for updates. Periodically, Amazon Monitron releases new application versions that you'll need to manually update if you don't enable automatic updates. These notifications will be provided on the web app as they become available. Flexible and immediate updates Amazon Monitron provides two kinds of in-app updates: flexible and immediate. Flexible updates allow you to elect whether or not to update the Amazon Monitron app once you’ve signed in. Immediate updates contain security updates, and must be installed in order to use the app. You can install updates from the Amazon Monitron app, or directly from Google Play or the App Store. To manually install the latest updates: 1. Sign in to the Amazon Monitron app and choose Update. In-app updates 252 Amazon Monitron User Guide 2. When you select update, you'll be directed to Google Play or the App Store. Select Update or Install to start the update. In-app updates 253 Amazon Monitron User Guide 3. If you start the update process within the Amazon Monitron app, you'll see a success message in the app once the update has been installed. In-app updates 254 Amazon Monitron User Guide Note You will not see the success message if the update happens automatically, or if you initiate the update process within the App Store or Google Play. In-app updates 255 Amazon Monitron User Guide Viewing sensor measurements You can choose to view your sensor measurement data in two chart formats: scatter plot and line plot. The following image shows the scatter plot view on the top and the line plot view on the bottom. Note You can select your sensor measurement view from the Chart type menu in your mobile and web app. Viewing sensor measurements 256 Amazon Monitron User Guide Viewing sensor measurements 257 Amazon Monitron User Guide Understanding sensor measurements When a sensor is initially paired to an asset, Amazon Monitron will learn from the vibration and temperature data collected from the equipment, establishing a baseline to determine what is "normal" for that asset. It will use this learning to detect potential failures in the future. Depending on the situation, operational scenario, use case, and various parameters like the asset’s duty cycle, Amazon Monitron will take between 14 and 21 days to establish this baseline. During this initial learning and training phase, the asset is assumed to be healthy. After establishing a baseline for the asset, Amazon Monitron monitors the data it collects, looking for an event or trend that indicates a potential failure. It specifically watches for increases in temperature, or vibration levels, or both. Increases in temperature and vibrations are two of the main indicators of a malfunctioning machine. Machine abnormalities often indicate that an asset is starting to fail. Amazon Monitron uses vibration thresholds established by the International Organization of Standardization (ISO) for your class of machinery. It applies the ISO thresholds in combination with its self-training model to assess actual thresholds to fit your equipment. For example, if your machine runs a little hot or a little cold, or if it vibrates a little bit more than standard, Amazon Monitron adjusts the thresholds slightly so that it can more accurately identify when the machine is acting abnormally. The only alarms you will recieve during the initial learning and training period will be from the ISO model (which does not require any learning period). You should treat ISO alarms during the training period as you would any alarm—acknowledge the alarm, perform any necessary review of the machine, and then close out the alarm with the suitable action taken code. After that time, Amazon Monitron continues to fine-tune the baseline, building a better picture of "normal" as the sensor collects more data. If temperature or vibration levels inconsistently rise above the modified threshold, a failure might be possible, but is probably not imminent. In that case, Amazon Monitron sends a Warning notification. If the increase is consistently above the threshold, the conditions are clearly abnormal and a failure is much more likely. Under those circumstances, Amazon Monitron sends an Alarm notification to the mobile or web app. Understanding sensor measurements 258 Amazon Monitron User Guide a web app notification a mobile app notification Understanding sensor measurements 259 Amazon Monitron User Guide In this example, the Position 3 sensor has detected a persistent increase in temperature and vibration, indicating that a potential failure needs to be investigated. Understanding sensor measurements 260 Amazon Monitron User Guide Understanding sensor measurements 261 Amazon Monitron User Guide Understanding asset status When a sensor detects a machine abnormality, the status of the asset changes. When a problem occurs, you can see it in the Assets list in the Amazon Monitron app. Topics • The Assets list |
AmazonMonitronUG-044 | AmazonMonitronUG.pdf | 44 | 258 Amazon Monitron User Guide a web app notification a mobile app notification Understanding sensor measurements 259 Amazon Monitron User Guide In this example, the Position 3 sensor has detected a persistent increase in temperature and vibration, indicating that a potential failure needs to be investigated. Understanding sensor measurements 260 Amazon Monitron User Guide Understanding sensor measurements 261 Amazon Monitron User Guide Understanding asset status When a sensor detects a machine abnormality, the status of the asset changes. When a problem occurs, you can see it in the Assets list in the Amazon Monitron app. Topics • The Assets list • Asset and position status • Notifications The Assets list The Assets list displays every asset in your site or project, showing the assets for the site or project that you are currently viewing. For more information about sites and projects, see Navigating between projects and sites in the mobile app. When you open the Amazon Monitron mobile app, it displays the list of assets associated with the site or project that you last worked with. To navigate to the Assets list from elsewhere in the app, use the following procedure. To open the assets list in either the mobile app or the web app 1. Choose the menu icon (☰). 2. Choose Assets. The assets list is displayed. Understanding asset status 262 the assets list in the web app Amazon Monitron User Guide The Assets list 263 Amazon Monitron User Guide the assets list in the mobile app Asset and position status The Assets list shows the status of each listed asset with an icon, as shown in the following table. Status Meaning Healthy state: The status of all sensor positions on the asset is healthy. Warning state: A warning has been triggered for one of the positions of this asset, indicating that Amazon Monitron has detected early signs of potential failure. Amazon Monitron identifies warning condition s by analyzing equipment vibration and temperatu re, using a combination of machine learning and ISO vibration standards. Alarm state: An alarm has been triggered for one of the positions of this asset, indicating that the machine vibration and temperature is out of the normal range at this position. We recommend that you investigate the issue at the earliest opportuni ty. An equipment failure Asset and position status 264 Amazon Monitron Status No sensor User Guide Meaning might occur if the issue isn't addressed. Acknowledged state: The warning or alarm state of the position has been acknowled ged by a technician, but the asset has not yet been fixed. No sensor: At least one position for the asset doesn't currently have a sensor paired to it. To learn more about a problem, choose the asset and look at the status of underlying sensor positions. Asset and position status 265 Amazon Monitron User Guide Amazon Monitron uses icons similar to the asset status icons to show the status of sensor positions. Status Meaning The position is healthy. All measured values are within the normal range. Warning state: A warning has been triggered for one of the positions of this asset, indicatin g that Amazon Monitron has detected early signs of potential failure. Amazon Monitron identifies warning conditions by analyzing equipment vibration and temperature, using a combination of machine learning and ISO vibration standards. Asset and position status 266 Amazon Monitron Status No sensor Notifications User Guide Meaning An alarm has been triggered for this position, indicating that the machine vibration and temperature is out of the normal range at this position. We recommend that you investiga te the issue at the earliest opportunity. An equipment failure might occur if the issue isn't addressed. The warning or alarm state of the position has been acknowledged by a technician, but not yet fixed. No sensors are currently paired with the position. When a warning or an alarm alert is generated, Amazon Monitron sends a notification to the admin user and technician in the app. Authorized personnel can also see notifications by choosing the notification icon in the mobile app when it displays an alert symbol ( ). Choosing the notification icon opens the Notifications page, which lists all pending notifications. Notifications 267 Amazon Monitron User Guide a notification in the web app a notification in the mobile app Notifications 268 Amazon Monitron User Guide Acknowledging a machine abnormality After receiving a notification, the admin user or technician must acknowledge it. Acknowledging the notification lets other users know that the issue has been noted and that action will be taken. Topics • To view and acknowledge a machine abnormality To view and acknowledge a machine abnormality 1. 2. From the Assets list, choose the asset that is reporting an abnormality. To view the issue, choose the position with the abnormality. Sensor measurements that show the anomaly |
AmazonMonitronUG-045 | AmazonMonitronUG.pdf | 45 | Guide a notification in the web app a notification in the mobile app Notifications 268 Amazon Monitron User Guide Acknowledging a machine abnormality After receiving a notification, the admin user or technician must acknowledge it. Acknowledging the notification lets other users know that the issue has been noted and that action will be taken. Topics • To view and acknowledge a machine abnormality To view and acknowledge a machine abnormality 1. 2. From the Assets list, choose the asset that is reporting an abnormality. To view the issue, choose the position with the abnormality. Sensor measurements that show the anomaly are displayed. Acknowledging a machine abnormality 269 Amazon Monitron User Guide 3. Choose Acknowledge. The status of the asset changes to Maintenance. To view and acknowledge a machine abnormality 270 Amazon Monitron User Guide Resolving an abnormality After an abnormality has occurred and been acknowledged, it must be addressed. You might fix it yourself, or call in a specialist. After the machine that reported the abnormality has been fixed, resolve the abnormality in the Amazon Monitron app. Resolving an abnormality returns the sensor to a healthy state. It also sends Amazon Monitron information about the problem so it can better predict similar abnormalities. You can choose from among many common types of failure (called failure modes) and causes of failures. If none of the modes or causes apply to your situation, choose Other. Topics • Failure modes • Failure causes • To resolve a machine abnormality using the mobile app Failure modes The following are the Amazon Monitron failure modes or types: • No failure detected (mute alert): Alert won't trigger if same abnormal condition is detected • Blockage: Obstruction that causes restrictive operation • Cavitation: Loss of pump suction pressure • Corrosion: Moist corrosion, fretting corrosion, false brinelling • Deposit: Build up of particles • Imbalance: Rotating component out of balance • Lubrication: Insufficient lubrication or improper lubrication • Misalignment: Rotating assembly is not aligned • Other • Resonance: External vibration sources • Rotating looseness: Rotating components like fan blade or pulley loose • Structural looseness: Mounting of component is loose Resolving an abnormality 271 Amazon Monitron User Guide • Transmitted fault: Caused by external forces • Undetermined (keep monitoring): Alert will trigger if same abnormal condition is detected. Failure causes The following are the Amazon Monitron failure causes: • Administrtion: Operator error • Design: Manufacturer design insufficient • Fabrication: Asset was modified from original state • Maintenance: Lack of maintenance performed on asset • Operation: Operation state change • Other: Storage, transportation (vibration/shock), bearing selection. manufacturing concerns, material concerns • Quality: Manufacturer quality insufficient • Undetermined: No root cause determined • Wear: Breakdown/Degradation over time To resolve a machine abnormality using the mobile app 1. From the Assets list, choose the asset that had an abnormality that you resolved. 2. Choose the position with the abnormality. 3. Choose Resolve. 4. For Failure mode, choose the type of failure that occurred. Failure causes 272 Amazon Monitron User Guide 5. 6. For Failure cause, choose the cause of the failure. For Action taken, choose which action you took. 7. Choose Submit. Taking a one-time measurement In addition to viewing the measurements that a sensor normally makes, you can take a one-time measurement with a sensor at any time. Important You can only take a sensor meaurement using the Amazon Monitron mobile app. Both admins and technicians can take this action. Topics • To take a one-time measurement (mobile app only) Taking a one-time measurement 273 Amazon Monitron User Guide To take a one-time measurement (mobile app only) 1. From the Amazon Monitron mobile app, select your project. To take a one-time measurement (mobile app only) 274 Amazon Monitron User Guide To take a one-time measurement (mobile app only) 275 Amazon Monitron User Guide 2. From the Amazon Monitron projects menu, select Assets . To take a one-time measurement (mobile app only) 276 Amazon Monitron User Guide To take a one-time measurement (mobile app only) 277 Amazon Monitron User Guide 3. From the list of assets, choose the asset that is paired to the sensor whose measurement you want to take. To take a one-time measurement (mobile app only) 278 Amazon Monitron User Guide To take a one-time measurement (mobile app only) 279 Amazon Monitron User Guide 4. Then, select the sensor you want to take the measurement with. To take a one-time measurement (mobile app only) 280 Amazon Monitron User Guide To take a one-time measurement (mobile app only) 281 Amazon Monitron User Guide 5. On the sensor page, from Sensor details, choose Actions. To take a one-time measurement (mobile app only) 282 Amazon Monitron User Guide To take a one-time measurement (mobile app only) 283 Amazon Monitron User Guide 6. From Actions, choose Take measurement. To take a one-time |
AmazonMonitronUG-046 | AmazonMonitronUG.pdf | 46 | Monitron User Guide To take a one-time measurement (mobile app only) 279 Amazon Monitron User Guide 4. Then, select the sensor you want to take the measurement with. To take a one-time measurement (mobile app only) 280 Amazon Monitron User Guide To take a one-time measurement (mobile app only) 281 Amazon Monitron User Guide 5. On the sensor page, from Sensor details, choose Actions. To take a one-time measurement (mobile app only) 282 Amazon Monitron User Guide To take a one-time measurement (mobile app only) 283 Amazon Monitron User Guide 6. From Actions, choose Take measurement. To take a one-time measurement (mobile app only) 284 Amazon Monitron User Guide To take a one-time measurement (mobile app only) 285 Amazon Monitron User Guide 7. Hold your smartphone close to the sensor. To take a one-time measurement (mobile app only) 286 Amazon Monitron User Guide To take a one-time measurement (mobile app only) 287 Amazon Monitron User Guide 8. When the measurement has been taken, move your smartphone away from the sensor. The new measurement is added to the data that the sensor has already collected. To take a one-time measurement (mobile app only) 288 Amazon Monitron User Guide Managing users After creating a project, you need to assign at least one admin user to help manage it. You can also add admin users to a project or remove them from a project later. After using the console to add the first admin user, you can add additional admin users with the Amazon Monitron mobile app. Important Amazon Monitron requires an email address for each app user. If you use directories like Microsoft Active Directory or an external ID provider, you need to make sure that email addresses for your users are added and synced. After creating a project or site, you need to add users to them. As an admin user, you can add users to three different roles: Admin, Technician, or Viewer. A user's role determines what they can do with Amazon Monitron. The extent of their role permissions is determined by whether they are added at the project level or at the site level. Setting a user's role at the project level gives the user permissions across all sites in that project. Setting a user's role at the site level gives the user permissions only to that site. Topics • Managing admin users • Managing non-admin users Managing admin users After creating a project, you need to assign at least one admin user to help manage it. You can also add admin users to a project or remove them from a project later. After using the console to add the first admin user, you can add additional admin users with the Amazon Monitron mobile app. Important Amazon Monitron requires an email address for each app user. If you use directories like Microsoft Active Directory or an external ID provider, you need to make sure that email addresses for your users are added and synced. Managing admin users 289 User Guide Amazon Monitron Topics • User directory setup • Adding users as an admin • Managing users as an admin user • Removing an admin user • Sending an email invitation User directory setup Amazon Monitron uses AWS IAM Identity Center to manage user access. Users are added from this IAM Identity Center user directory. How you add an admin user depends on how IAM Identity Center has been set up for your organization. Important Amazon Monitron requires an email address for each app user. If you use directories like Microsoft Active Directory or an external ID provider, you need to make sure that email addresses for your users are added and synced. Topics • Understanding SSO requirements • Adding admin users using the native IAM Identity Center directory • Adding admin users using Microsoft Active Directory • Adding admin users using an external ID provider • Returning to Amazon Monitron with IAM Identity Center Understanding SSO requirements When you create a project, Amazon Monitron automatically detects whether IAM Identity Center has been enabled and configured on your account and whether all prerequisites for using IAM Identity Center with Amazon Monitron are satisfied. If not, Amazon Monitron produces an error and User directory setup 290 Amazon Monitron User Guide provides a list of prerequisites that are needed. You must meet all prerequisites before you can add admin users. For more information about enabling and configuring IAM Identity Center for your organization, see AWS Single Sign-On. Important Amazon Monitron supports all IAM Identity Center regions except opt-in and government regions. The list of regions supported are: • US East (N. Virginia) • US East (Ohio) • US West (N. California) • US West (Oregon) • Asia Pacific (Mumbai) • Asia Pacific (Tokyo) • Asia Pacific (Seoul) • Asia Pacific (Osaka) • |
AmazonMonitronUG-047 | AmazonMonitronUG.pdf | 47 | and User directory setup 290 Amazon Monitron User Guide provides a list of prerequisites that are needed. You must meet all prerequisites before you can add admin users. For more information about enabling and configuring IAM Identity Center for your organization, see AWS Single Sign-On. Important Amazon Monitron supports all IAM Identity Center regions except opt-in and government regions. The list of regions supported are: • US East (N. Virginia) • US East (Ohio) • US West (N. California) • US West (Oregon) • Asia Pacific (Mumbai) • Asia Pacific (Tokyo) • Asia Pacific (Seoul) • Asia Pacific (Osaka) • Asia Pacific (Singapore) • Asia Pacific (Sydney) • Canada (Central) • Europe (Frankfurt) • Europe (Ireland) • Europe (London) • Europe (Paris) • Europe (Stockholm) • South America (São Paulo) IAM Identity Center prerequisites Before you can set up IAM Identity Center, you must: • Have first set up the AWS Organizations service and have All features set to enabled. For more information about this setting, see Enabling All Features in Your Organization in the AWS Organizations User Guide. User directory setup 291 Amazon Monitron User Guide • Sign in with the AWS Organizations management account credentials before you begin setting up IAM Identity Center. These credentials are required to enable IAM Identity Center. For more information, see Creating and Managing an AWS Organization in the AWS Organizations User Guide. You cannot set up IAM Identity Center while signed in with credentials from an Organization’s member account. • Have chosen an identity source to determine which pool of users has SSO access to the user portal. If you choose to use the default IAM Identity Center identity source for your user store, no prerequisite tasks are required. The IAM Identity Center store is created by default once you enable IAM Identity Center and is immediately ready for use. There is no cost for using this store. Alternatively, you can choose to Connect to your external identity provider using Azure Active Directory. If you choose to connect to an existing Active Directory for your user store, you must have the following: • An existing AD Connector or AWS Managed Microsoft AD directory set up in AWS Directory Service, and it must reside within your organization's management account. You can connect only one AWS Managed Microsoft AD directory at a time. However, you can change it to a different AWS Managed Microsoft AD directory or change it back to an IAM Identity Center store at any time. For more information, see Create a AWS Managed Microsoft AD Directory in the AWS Directory Service Administration Guide. • Set up IAM Identity Center in the Region where your AWS Managed Microsoft AD directory is set up. IAM Identity Center stores the assignment data in the same Region as the directory. To administer IAM Identity Center, you should switch to the Region where you have setup IAM Identity Center. Also, note that IAM Identity Center’s user portal uses the same access URL as your connected directory. • If you currently filter access to specific Amazon Web Service (AWS) domains or URL endpoints using a web content filtering solution such as next-generation firewalls (NGFW) or secure web gateways (SWG), you must add the following domains and/or URL endpoints to your web- content filtering solution allow-lists in order for IAM Identity Center to work properly: Specific DNS domains • *.awsapps.com (http://awsapps.com/) • *.signin.aws Specific URL End-points • https://[yourdirectory].awsapps.com/start • https://[yourdirectory].awsapps.com/login User directory setup 292 Amazon Monitron User Guide • https://[yourregion].signin.aws/platform/login We highly recommend that before you enable IAM Identity Center you first check to see if your AWS account is approaching the quota limit for IAM roles. For more information, see IAM object quotas. If you are nearing the quota limit, consider increasing the quota. Otherwise, you may have issues with IAM Identity Center as you provision permission sets to accounts that have exceeded the IAM role limit. Adding admin users using the native IAM Identity Center directory The simplest way to add admin users to your project is by using the IAM Identity Center native directory. You can use it by starting to use Amazon Monitron and letting it configure IAM Identity Center at a basic level for you. You can also set up IAM Identity Center before using Amazon Monitron and set it to use the native directory. Either way, you can add users manually and without potentially exposing user identity information to other admin users beyond name and email. To add an admin user when using the native IAM Identity Center directory 1. Open the Amazon Monitron console at https://console.aws.amazon.com/monitron . 2. Choose Create Project. 3. In the navigation pane, choose the project you want. 4. On the Users page, choose the users that you want to assign as admin users. If you can't |
AmazonMonitronUG-048 | AmazonMonitronUG.pdf | 48 | level for you. You can also set up IAM Identity Center before using Amazon Monitron and set it to use the native directory. Either way, you can add users manually and without potentially exposing user identity information to other admin users beyond name and email. To add an admin user when using the native IAM Identity Center directory 1. Open the Amazon Monitron console at https://console.aws.amazon.com/monitron . 2. Choose Create Project. 3. In the navigation pane, choose the project you want. 4. On the Users page, choose the users that you want to assign as admin users. If you can't see a user, search for them. The users you choose are displayed in the Selected users section. 5. If the user you want isn't in the directory, choose Create user to add the user. 1. Under Create a user, for Email, enter the new admin user's email address. User directory setup 293 Amazon Monitron User Guide 2. For First name and Last name, enter the admin's name. 3. Choose Create User. 6. When the user's name appears in the directory list, choose Add to add the admin users you've selected. 7. Email the admin users an invitation to the project that includes a link to download the Amazon Monitron mobile app. For more information, see Sending an email invitation. Amazon Monitron takes you to the project page for your project, where it lists all admin users. 8. To add additional admin users, choose Add admin. Any admin user can add other users using the Amazon Monitron mobile app. For more information, see Adding a User in the Amazon Monitron User Guide. Adding admin users using Microsoft Active Directory If you use Microsoft Active Directory (AD) for your organization's primary user directory, you can configure IAM Identity Center to use it. IAM Identity Center enables you to connect your self- managed Active Directory as your AWS Managed Microsoft AD directory using AWS Directory Service. This Microsoft AD directory provides you with the pool of identities that you can pull from when using the Amazon Monitron console (or Amazon Monitron mobile app) to assign user roles. User directory setup 294 Amazon Monitron Important User Guide Amazon Monitron requires an email address for each app user. Make sure that email addresses for your users are added and synced. All Amazon Monitron admin users have access to identity information in the user directory that is configured in IAM Identity Center for Amazon Monitron. We strongly recommend using an isolated directory if you want to limit access to user organization information. To add an admin user using Microsoft Active Directory 1. Configure IAM Identity Center to connect with your Microsoft Active Directory. The steps involved in this differ depending on whether you're using a self-managed Active Directory or an AWS Managed Microsoft AD directory. For more information, see Connect to Microsoft AD Directory. 2. Open the Amazon Monitron console at https://console.aws.amazon.com/monitron . 3. Choose Create Project. 4. 5. In the navigation pane, choose the project you want. For Active directory domain, choose the directory domain from which you want to add identities. User directory setup 295 Amazon Monitron User Guide 6. Choose Users or Groups, depending on how you want to search the user directory. 7. Enter a string in the search box to find the identity you want to add and then choose Search. To limit the number of users returned, enter a longer string in the search box. For example, if you enter "olg" in the search box, the list returns all users with the letters "olg" in their names, such as "Olga Kurth" and "Jamie Folgman." 8. Choose the users you want to assign as admin users. 9. Choose Add to add the admin users. Adding admin users using an external ID provider If you're using an external Identity provider (IdP), you can configure IAM Identity Center to use that provider through the Security Assertion Markup Language (SAML) 2.0 standard. This provides you with the pool of identities in your IdP directory. You can pull this pool when using the Amazon Monitron console (or Amazon Monitron mobile app) and assign them as admin users. This also enables your users to sign in to Amazon Monitron with their corporate credentials. User directory setup 296 Amazon Monitron Important User Guide Amazon Monitron requires an email address for each app user. Make sure that email addresses for your users are added and synced. All Amazon Monitron admin users have access to identity information in the user directory that is configured in IAM Identity Center for Amazon Monitron. We strongly recommend using an isolated directory if you want to limit access to user organization information. To add an admin user using an external ID provider (IdP) 1. Configure AWS IAM Identity Center to connect with your |
AmazonMonitronUG-049 | AmazonMonitronUG.pdf | 49 | to Amazon Monitron with their corporate credentials. User directory setup 296 Amazon Monitron Important User Guide Amazon Monitron requires an email address for each app user. Make sure that email addresses for your users are added and synced. All Amazon Monitron admin users have access to identity information in the user directory that is configured in IAM Identity Center for Amazon Monitron. We strongly recommend using an isolated directory if you want to limit access to user organization information. To add an admin user using an external ID provider (IdP) 1. Configure AWS IAM Identity Center to connect with your external IdP. The steps involved in this differ based on the provider you're using. For more information, see Connect to Your External ID Provider. 2. Open the Amazon Monitron console at https://console.aws.amazon.com/monitron . 3. Choose Create Project. 4. In the navigation pane, choose the project you want. 5. On the Users page, choose the users that you want to assign as admin users. If you can't see a user, search for them. 6. Choose Add to add the admin users. User directory setup 297 Amazon Monitron User Guide Returning to Amazon Monitron with IAM Identity Center When you log out of the Amazon Monitron web app, you may still be signed in to AWS IAM Identity Center. Any other applications that you have opened from the user portal remain open and running. There are two ways to log out of IAM Identity Center: • Log out directly through the IAM Identity Center portal. • Once an hour, AWS IAM Identity Center checks to see if you are actively using any AWS services. If you are not, then you are logged out of IAM Identity Center automatically. To learn about admin users using IAM Identity Center, see User directory setup. To learn about security best practices with Amazon Monitron and IAM Identity Center, see Security best practices for Amazon Monitron. To learn about using the SSO user portal, see Using the user portal. User directory setup 298 Amazon Monitron User Guide Adding users as an admin As an admin, you can add other users (including other admin users) in the Amazon Monitron web app. 1. Navigate to the project or site that you want to add a user to, and then to the Users list. 2. Enter a user name. Amazon Monitron searches the user directory for the user. Choose the user from the list and the role you want to assign to the user: Admin, Technician, or Viewer. Then, choose Add user. Adding users as an admin 299 Amazon Monitron User Guide 3. The new user appears on the Users list. Adding users as an admin 300 Amazon Monitron User Guide Send the new user an email invitation with a link for accessing the project and downloading the Amazon Monitron mobile app. For more information, see Sending an email invitation. Managing users as an admin user As an admin, you can use the list of users to manage users in the Amazon Monitron web app. As project level admin, you can view all users at the project level and all users at a particular site level. The Users & Permissions page displays the following information to make user management easier: • Name – The name of the user. • Role – The role assigned to the user, whether Admin, Technician, Viewer, or any combination of these. • Assigned locations – The number of locations the user is assigned to. • Project level access – Whether the user has project level access or only specific site level access. Managing users as an admin user 301 Amazon Monitron User Guide 1. Navigate to the project or site that you want to add a user to or update user permissions from, and then to the Users & Permissions list. 2. Select Edit. Then, from the Modify user permissions page, in Username, select the user whose details you want to view or edit. Amazon Monitron displays the list of locations the user is assigned to. Managing users as an admin user 302 Amazon Monitron User Guide 3. To change the role assigned to the user, select between Admin, Technician, and Viewer. Or, you can choose to Remove the user. Then, select Done. Managing users as an admin user 303 Amazon Monitron User Guide Amazon Monitron diplays how the user was assigned permissions to all locations. If a user is assigned an Admin role at the project level, they inherit access to all locations within that project. In this case, Amazon Monitron indicates their access level as Admin – inherited. Managing users as an admin user 304 Amazon Monitron User Guide Removing an admin user Every project must have at least one admin user. Before removing an admin user from a project, make sure that |
AmazonMonitronUG-050 | AmazonMonitronUG.pdf | 50 | choose to Remove the user. Then, select Done. Managing users as an admin user 303 Amazon Monitron User Guide Amazon Monitron diplays how the user was assigned permissions to all locations. If a user is assigned an Admin role at the project level, they inherit access to all locations within that project. In this case, Amazon Monitron indicates their access level as Admin – inherited. Managing users as an admin user 304 Amazon Monitron User Guide Removing an admin user Every project must have at least one admin user. Before removing an admin user from a project, make sure that there is at least one other admin user assigned to it. Topics • To remove an admin user To remove an admin user 1. Open the Amazon Monitron console at https://console.aws.amazon.com/monitron . 2. Choose Create Project. 3. In the navigation pane, choose the project you want. Removing an admin user 305 Amazon Monitron User Guide 4. From the Admin Users list, choose the user that you want to remove. 5. Choose Remove. 6. Choose Remove again. The user is removed from the list of admin users for that project. Sending an email invitation When you add a user to an Amazon Monitron project or site, you send them an email and invite them to download and log in to the Amazon Monitron mobile or web app. This invitation also contains instructions for connecting to your project. Topics • To generate an email invitation to a site or project using the mobile app • To generate an email invitation to a site or project using the web app To generate an email invitation to a site or project using the mobile app 1. Add the user to the site or project. 2. Choose the vertical ellipse icon ( next to the user that you added. 3. Choose Email instructions. ) Sending an email invitation 306 Amazon Monitron User Guide Your email application opens with a draft of the email invitation addressed to that user. It contains two links. One link is to download the Amazon Monitron mobile app from the Google Play Store. The other is to open the project to which the user has been added. 4. Verify that the email is correct, and then send it to the user. To generate an email invitation to a site or project using the web app 1. Add the user to the site or project. 2. Choose Users from the left nav. 3. Choose Email instructions. 4. Your email application opens with a draft of the email invitation addressed to that user. It contains two links. One is to download the Amazon Monitron mobile app from the Google Play Store. The other link opens the project to which the user has been added. 5. Verify that the email is correct, and then send it to the user. Warning Beware of phishing attacks. An attacker may send an email impersonating a Amazon Monitron project invitation email to your users. Warn them to make sure that the directory name is visible on the login screen before entering their sign-in credentials. Managing non-admin users After creating a project or site, you need to add users to them. As an admin user, you can add users to three different roles: Admin, Technician, or Viewer. A user's role determines what they can do with Amazon Monitron. The extent of their role permissions is determined by whether they are added at the project level or at the site level. Setting a user's role set at the project level gives the user permissions across all sites in that project. Setting a user's role at the site level gives the use permissions only to that site. Topics • Displaying a list of users • Adding a user Managing non-admin users 307 Amazon Monitron • Changing a user role • Removing a user Displaying a list of users User Guide As an admin, you can use the list of users to manage users in the Amazon Monitron app. There are three levels you can choose from (depending on your admin role) to view a list of users: • As project level admin, you can view all users at the project level. • As project level admin, you can view all users at a particular site level. • As site level admin, you can view all users at a particular site level. Topics • To display the list of users in the mobile app • To display the list of users in the web app To display the list of users in the mobile app 1. Log into the Amazon Monitron mobile app on your smartphone. 2. Choose the project or site whose users you want to view. 3. Choose the menu icon (☰). Displaying a list of users 308 Amazon Monitron |
AmazonMonitronUG-051 | AmazonMonitronUG.pdf | 51 | • As project level admin, you can view all users at a particular site level. • As site level admin, you can view all users at a particular site level. Topics • To display the list of users in the mobile app • To display the list of users in the web app To display the list of users in the mobile app 1. Log into the Amazon Monitron mobile app on your smartphone. 2. Choose the project or site whose users you want to view. 3. Choose the menu icon (☰). Displaying a list of users 308 Amazon Monitron 4. Choose Users. User Guide A list of all users associated with the project or site is displayed. To display the list of users in the web app The Users & Permissions page displays the following information to make user management easier: • Name – The name of the user. • Role – The role assigned to the user, whether Admin, Technician, Viewer, or any combination of these. • Assigned locations – The number of locations the user is assigned to. • Project level access – Whether the user has project level access or only specific site level access. 1. 2. Log into the Amazon Monitron web app. Select Users from the left nav. The list of users will appear. 3. Choose the project or site whose users you want to view. Displaying a list of users 309 Amazon Monitron User Guide A list of all users associated with the project or site is displayed. Adding a user When you add a new user, the role you choose determines the permissions that user has. Users can have the following roles: • Admin. An admin user has full access to all resources within the project or site to which they've been added. They can add other users, create assets, pair sensors to assets, and so on. They can also monitor assets and acknowledge and resolve abnormalities. If they are added at the project level, these permissions extend through the entire project. If they are added at the site level, these permissions are limited to only that site. • Technician. A technician user has read-only permissions to the project or site to which they've been added and permissions for monitoring assets and acknowledging and resolving abnormalities. If they are added at the project level, these permissions extend through the entire project. If they are added at the site level, these permissions are for only that site. • Read only. A user with read-only permissions has permission to read (but not add, change, or delete) details of all resources within the project or site to which they've been added. You use the same procedure to add a new user to a project or to a site. Topics • To add a user using the mobile app Adding a user 310 Amazon Monitron • To add a user using the web app To add a user using the mobile app User Guide 1. Log into the Amazon Monitron mobile app on your smartphone. 2. Navigate to the project or site that you want to add a user to, and then to the Users list. 3. Choose Add user. 4. Enter a user name. Amazon Monitron searches the user directory for the user. 5. Choose the user from the list. 6. Choose the role that you want to assign the user: Admin, Technician, or Viewer. 7. Choose Add. The new user appears on the Users list. 8. Send the new user an email invitation with a link for accessing the project and downloading the Amazon Monitron mobile app. For more information, see Sending an email invitation. To add a user using the web app 1. Navigate to the project or site that you want to add a user to, and then to the Users list. Adding a user 311 Amazon Monitron User Guide 2. Enter a user name. Amazon Monitron searches the user directory for the user. Choose the user from the list and the role you want to assign to the user: Admin, Technician, or Viewer. Then, choose Add user. Adding a user 312 Amazon Monitron User Guide 3. The new user appears on the Users list. Adding a user 313 Amazon Monitron User Guide Send the new user an email invitation with a link for accessing the project and downloading the Amazon Monitron mobile app. For more information, see Sending an email invitation. Changing a user role You can change a user's role, but not a user's name. That's because the name is linked to the user directory that is linked to by Amazon Monitron. To change a project or site's users, you must remove the previous users and add the new ones. For information on removing users from a project or site, |
AmazonMonitronUG-052 | AmazonMonitronUG.pdf | 52 | the Users list. Adding a user 313 Amazon Monitron User Guide Send the new user an email invitation with a link for accessing the project and downloading the Amazon Monitron mobile app. For more information, see Sending an email invitation. Changing a user role You can change a user's role, but not a user's name. That's because the name is linked to the user directory that is linked to by Amazon Monitron. To change a project or site's users, you must remove the previous users and add the new ones. For information on removing users from a project or site, see To remove a user using the mobile app. For information on adding new users, see Adding a user. Topics • To change a user role using the mobile app • To change a user role using the web app Changing a user role 314 Amazon Monitron User Guide To change a user role using the mobile app 1. Log into the Amazon Monitron mobile app on your smartphone. 2. Navigate to the project or site for the user whose role you want to change, and then to the Users list. 3. Choose the vertical ellipsis ( ) next to the name of the user whose role you want to change. 4. Choose Edit user. 5. Choose a new role for the user: Admin, Technician, or Read only. 6. Choose Save. To change a user role using the web app 1. Choose Users from the navigation pane. Changing a user role 315 Amazon Monitron 2. Choose Edit user role. 3. Choose a new role for the user: Admin, Technician, or Viewer. User Guide 4. Choose Save. Removing a user Removing a user removes their permissions to access the site or project. It doesn't affect the user directory. Additionally, if the user has permissions to other sites or projects, this won't remove those permissions. Topics • To remove a user using the mobile app • To remove a user using the web app To remove a user using the mobile app 1. Log into the Amazon Monitron mobile app on your smartphone. Removing a user 316 Amazon Monitron User Guide 2. Navigate to the project or site, and then to the Users list page. 3. Choose the vertical ellipses ( ) next to the user name. 4. Choose Remove user. 5. On the Confirmation page, choose Remove. To remove a user using the web app 1. Select Users from the nav pane. 2. Select the user that you want to remove. 3. Choose Remove. Removing a user 317 Amazon Monitron User Guide Understanding networking with Amazon Monitron As you plan your local network, and make decisions about how that network includes Amazon Monitron, it may be helpful to understand how each component relates to the others. Topics • Networking with your mobile device • Securing your network Networking with your mobile device From a networking perspective, the process of provisioning sensors or gateways goes like this. Topics • Setting up your Monitron network foundation with your mobile app • Setting up your gateways • Setting up your sensors Setting up your Monitron network foundation with your mobile app 1. Your mobile device uses Wi-Fi or a signal from outside the facility (such as a satellite or a tower) to connect to the internet. 2. Over the internet, you install the Amazon Monitron mobile app on your mobile device. (This only has to be done once per device.) 3. Over the internet, the Monitron app on your mobile device connects to the AWS infrastructure, authenticating with AWS IAM Identity Center. 4. Having been authenticated inside the AWS infrastructure, the app connects to the Amazon Monitron back end. 5. Using your authenticated app, you identify the framework of your local Amazon Monitron setup. This involves naming your local network and identifying how many gateways will be part of it. Networking with your mobile device 318 Amazon Monitron User Guide Setting up your gateways 1. In your mobile app, (running authenticated and securely over the internet), choose the option for adding a gateway. 2. You give your mobile app permission to access Bluetooth functionality on your mobile device. 3. The mobile app on your device, using Bluetooth, connects to your local gateway. 4. You give the app the name of your local network (Wi-Fi only). 5. You give the app the password to your local network. 6. The app, securely over the internet, communicates with the Monitron back end about your gateway. 7. On the front end, through Bluetooth on your mobile device, the app gives the gateway the token it needs to communicate with the Monitron back end. 8. The gateway uses your local network (Ethernet or Wi-Fi) to connect to the internet through your local internet access point. 9. Securely, over the |
AmazonMonitronUG-053 | AmazonMonitronUG.pdf | 53 | device, using Bluetooth, connects to your local gateway. 4. You give the app the name of your local network (Wi-Fi only). 5. You give the app the password to your local network. 6. The app, securely over the internet, communicates with the Monitron back end about your gateway. 7. On the front end, through Bluetooth on your mobile device, the app gives the gateway the token it needs to communicate with the Monitron back end. 8. The gateway uses your local network (Ethernet or Wi-Fi) to connect to the internet through your local internet access point. 9. Securely, over the internet, your gateway registers itself with the Monitron back end. 10.A representation of your gateway now appears in your app as a part of your network. Setting up your sensors 1. In the mobile app, you indicate the name and class of your asset (once per asset). 2. In the mobile app, you give a name to a sensor. 3. In your facility, you physically attach an un-paired sensor to your asset. 4. From the mobile app, using your device’s NFC, you connect to the sensor. 5. The mobile app, using your device’s NFC, tells the sensor about your local Monitron gateway, already set up. 6. The mobile app, securely over the internet, tells the Monitron back end about the sensor. 7. The sensor, using Bluetooth, begins to send data about the asset to the gateway. 8. The gateway, securely over the internet, sends the sensor’s data to the Monitron back end. 9. In the mobile app (or the web app), securely over the internet, you can now view the analytical data about your asset. Setting up your gateways 319 Amazon Monitron User Guide Securing your network In order to allow your Amazon Monitron gateways to send data back to AWS, you should allow the following with regard to your local network traffic: • Protocol UDP, port 53 - standard DNS port • Protocol UDP, ports 67 and 68 - standard DHCP ports • TCP ports 443 and 8883 • For Amazon Monitron gateways commissioned before 19th January, 2024: • Domains ending in *.amazonaws.com • For Amazon Monitron gateways commissioned after 19th January, 2024: • Asia Pacific (Sydney) (ap-southeast-2) – 54.79.215.104 and 54.79.23.89 • Europe (Ireland) (eu-west-1) – 54.72.131.46, 34.251.27.192, and 52.213.71.97 • US East (N. Virginia) (us-east-1) – 3.215.69.205, 52.86.131.66, and 18.210.44.199 Note There's no regression with new static IPs being enabled by default for previously commissioned devices as they have already been allow listed for IP domains ending in *.amazonaws.com (which already includes the new static IP domain of amazonaws.com). Decommissioning and recomissioning a gateway will switch it to static IP. You can't revert a gateway network configuration from a static IP to a dynamic IP. If you are using an Android mobile device to provision your gateways and sensors, then you should allow the following with regard to your local network traffic: • TCP ports 443, 5228, 5229, and 5230 • Domains ending in *.google.com, *.googleapis.com • Any ports required by your telecom provider • TCP port 5094 for SSL communications used on Vodafone devices Securing your network 320 Amazon Monitron User Guide If you are using an Apple mobile device to provision your gateways and sensors, then you should allow the following with regard to your local network traffic: • TCP ports 443, 2197, and 5223 • Subnets 17.249.0.0/16, 17.252.0.0/16, 17.57.144.0/22, 17.188.128.0/18, and 17.188.20.0/23 • See also: Apple’s list of required ports and hosts Note: Amazon Monitron, Android, and Apple do not (per their respective documentation) require the following ports to be open: • UDP port 443 • TCP port 80 Securing your network 321 Amazon Monitron User Guide Accessing your Amazon Monitron data There are two ways to access your raw Amazon Monitron data outside of Amazon Monitron. You may want access your data on an ongoing basis, so that you can use it elsewhere. In that case, you can configure Amazon Monitron to automatically add your data to a Kinesis stream. From there, you can port it to various destinations, including Amazon S3 and Lambda. This process requires configuration, and that configuration requires an understanding of Kinesis Data Streams. However, once you have all the elements arranged to your satisfaction, you can keep your data streaming automatically. Or you may want to access your data once in a while, just to gain a clear understanding of what kind of data you are storing and analyzing on AWS. In that case, you can ask AWS support to manually copy your data to Amazon S3. This process requires less configuration, but it cannot be automated. It only gives you the data that Amazon Monitron has accumulated up until now, in one chunk. Topics • Exporting your Amazon Monitron data to Amazon S3 • Amazon Monitron Kinesis |
AmazonMonitronUG-054 | AmazonMonitronUG.pdf | 54 | the elements arranged to your satisfaction, you can keep your data streaming automatically. Or you may want to access your data once in a while, just to gain a clear understanding of what kind of data you are storing and analyzing on AWS. In that case, you can ask AWS support to manually copy your data to Amazon S3. This process requires less configuration, but it cannot be automated. It only gives you the data that Amazon Monitron has accumulated up until now, in one chunk. Topics • Exporting your Amazon Monitron data to Amazon S3 • Amazon Monitron Kinesis data export v1 • Amazon Monitron Kinesis data export v2 Exporting your Amazon Monitron data to Amazon S3 You may sometimes want to access the raw data that Amazon Monitron is storing for you, in order to stay informed about exactly what kind of data you’re securely storing with AWS. You can get your raw data by filing a support ticket with AWS, and by giving Amazon Monitron permission to deliver your data to you. To get real time operational data for Amazon Monitron resources that can be consumed programmatically, consider exporting your data using Kinesis streams. For more information, see Amazon Monitron Kinesis data export v2. Topics • Prerequisites Exporting your data to Amazon S3 322 Amazon Monitron User Guide • Exporting your data with AWS CloudFormation (recommended option) • Exporting your data with the console • Exporting your data with CloudShell Prerequisites To successfully export your Amazon Monitron data, the following prerequisites must be met. • You must not already have another export (of Amazon Monitron data) running in the same region. • You cannot have run another export in same region in past 24 hours. Exporting your data with AWS CloudFormation (recommended option) Topics • Step 1: Create your Amazon S3 bucket, IAM role, and IAM policies. • Step 2: Note your resources • Step 3: Create the support case Step 1: Create your Amazon S3 bucket, IAM role, and IAM policies. 1. Sign in to your AWS account. 2. Open a new browser tab with the following URL. https://console.aws.amazon.com/cloudformation/home?region=us-east-1#/stacks/create/ review?templateURL=https://s3.us-east-1.amazonaws.com/monitron-cloudformation- templates-us-east-1/monitron_manual_download.yaml&stackName=monitronexport 3. On the AWS CloudFormation page that opens, in the upper right corner, select the region in which you are using Amazon Monitron. 4. Choose Create stack. Prerequisites 323 Amazon Monitron User Guide 5. On the next page, choose the refresh icon as often as you like until the status of the stack (monitronexport) is CREATE_COMPLETE. Exporting your data with AWS CloudFormation (recommended option) 324 Amazon Monitron User Guide Step 2: Note your resources 1. Choose the Outputs tab. 2. Note the value of the key MonRoleArn. 3. Note the value of the key S3BucketArn. 4. Note your account ID from the upper right corner of the page). 5. Note the region you chose in Step 1. It also now appears at the top of the page, to the left of your account ID. Exporting your data with AWS CloudFormation (recommended option) 325 Amazon Monitron User Guide Step 3: Create the support case 1. From your AWS console, choose the question mark icon near the upper right corner of any page, then choose Support Center. 2. On the next page, choose Create case. 3. On the How can we help? page, do the following: Exporting your data with AWS CloudFormation (recommended option) 326 Amazon Monitron User Guide a. Choose Account and billing support. b. Under Service, choose Account. c. Under Category, choose Compliance & Accreditations. d. Choose Severity, if that option is available to you based on your support subscription. e. Choose Next step: Additional information. 4. In Additional information do the following: a. Under Subject, enter Amazon Monitron data export request. b. In the Description field, enter: 1. your account ID 2. the region of the bucket you created 3. the ARN of the bucket you created (for example: "arn:aws:s3:::bucketname") Exporting your data with AWS CloudFormation (recommended option) 327 Amazon Monitron User Guide 4. the ARN of the role you created (for example: "arn:aws:iam::273771705212:role/role- for-monitron") c. Choose Next step: Solve now or contact us. 5. In Solve now or contact us do the following: a. In Solve now, select Next. Exporting your data with AWS CloudFormation (recommended option) 328 Amazon Monitron User Guide b. c. In Contact us, choose your Preferred contact language and preferred method of contact. Choose Submit. A confirmation screen with your case ID and details will be displayed. Exporting your data with AWS CloudFormation (recommended option) 329 Amazon Monitron User Guide An AWS customer support specialist will get back to you as soon as possible. If there are any issues with the steps listed, the specialist may ask you for more information. If all the necessary information has been provided, the specialist will let you know as soon as your |
AmazonMonitronUG-055 | AmazonMonitronUG.pdf | 55 | AWS CloudFormation (recommended option) 328 Amazon Monitron User Guide b. c. In Contact us, choose your Preferred contact language and preferred method of contact. Choose Submit. A confirmation screen with your case ID and details will be displayed. Exporting your data with AWS CloudFormation (recommended option) 329 Amazon Monitron User Guide An AWS customer support specialist will get back to you as soon as possible. If there are any issues with the steps listed, the specialist may ask you for more information. If all the necessary information has been provided, the specialist will let you know as soon as your data has been copied to the Amazon S3 bucket that you created above. Exporting your data with the console Topics • Step 1: Setting up your Amazon S3 bucket • Step 2: Give Amazon Monitron permission to access Amazon S3 • Step 3: Create the role • Step 4: Create the trust policy • Step 5: Create the support case Step 1: Setting up your Amazon S3 bucket 1. Open the Amazon S3 console. 2. Choose Create bucket. 3. Name your bucket and select an appropriate region. Then, at the bottom of the page, choose Create bucket. Exporting your data with the console 330 Amazon Monitron User Guide Important At this time, Amazon Monitron is only supported in three regions: • US East (N. Virginia) us-east-1 • EU (Ireland) eu-west-1 • Asia Pacific (Sydney) ap-south-east-2 Therefore, your Amazon S3 bucket must be in one of those regions. It must also be the same region in which you are using the Amazon Monitron service. 4. Review the rest of the options on the page, and make the appropriate choices, depending on your security needs and policies. Exporting your data with the console 331 Amazon Monitron User Guide Important You are responsible for taking the appropriate steps to secure your data. We strongly recommend using server-side encryption and blocking public access to your bucket. 5. Using the search box, find the bucket you just created, and then choose it. 6. From the Properties tab, make a note of the name, ARN, and region of the bucket. Exporting your data with the console 332 Amazon Monitron User Guide Step 2: Give Amazon Monitron permission to access Amazon S3 1. Open the IAM console and choose Policies. Exporting your data with the console 333 Amazon Monitron 2. Choose Create policy. User Guide 3. Select the JSON tab. Exporting your data with the console 334 Amazon Monitron User Guide 4. Delete the default JSON text so that the form is empty. 5. Paste in the bucket access policy. { "Statement": [ { "Action": [ "s3:GetBucketAcl", "s3:GetBucketLocation", "s3:ListBucket" ], "Effect": "Allow", "Resource": [ "arn:aws:s3:::bucketname" ] }, { Exporting your data with the console 335 Amazon Monitron User Guide "Action": [ "s3:PutObject", "s3:GetBucketAcl" ], "Effect": "Allow", "Resource": [ "arn:aws:s3:::bucketname/*" ] } ], "Version": "2012-10-17" } 6. Select Next. 7. On the Review and create page, do the following: Exporting your data with the console 336 Amazon Monitron User Guide a. b. c. In Policy details, enter a Policy name and optional Description. Leave the Permissions defined in this policy section as is. In Add tags — optional, you can choose to add tags to keep track of your resources.. d. Choose Create policy. Step 3: Create the role 1. Open the IAM console and choose Roles. Exporting your data with the console 337 Amazon Monitron User Guide 2. Choose Create role. 3. On the Select trusted entity, in Trusted entity type, choose AWS account. 4. In An AWS account, choose This account. You can customize additional setting using Options. 5. Choose Next. Exporting your data with the console 338 Amazon Monitron User Guide 6. In Add permissions, for Permissions policies, search for the policy you just created in the search box, and select your policy. Exporting your data with the console 339 Amazon Monitron User Guide 7. On the Name, review, and create page do the following: a. In Role details enter a Role name and optional Description. b. You can choose to ignore Step 1: Select trusted entities and Step 2: Add permisions. c. For Step 3: Add tags, for Add tags — optional, add optional tags to keep track of your resources. 8. Choose Create role. Exporting your data with the console 340 Amazon Monitron User Guide Exporting your data with the console 341 Amazon Monitron User Guide Step 4: Create the trust policy 1. Search for the role you just created and choose the role. 2. Select the Trust relationships tab. Exporting your data with the console 342 Amazon Monitron User Guide 3. Choose Edit trust relationship. Exporting your data with the console 343 Amazon Monitron User Guide 4. Erase the default JSON text so that the form is empty. 5. Paste in the |
AmazonMonitronUG-056 | AmazonMonitronUG.pdf | 56 | tags to keep track of your resources. 8. Choose Create role. Exporting your data with the console 340 Amazon Monitron User Guide Exporting your data with the console 341 Amazon Monitron User Guide Step 4: Create the trust policy 1. Search for the role you just created and choose the role. 2. Select the Trust relationships tab. Exporting your data with the console 342 Amazon Monitron User Guide 3. Choose Edit trust relationship. Exporting your data with the console 343 Amazon Monitron User Guide 4. Erase the default JSON text so that the form is empty. 5. Paste in the policy that allows Amazon Monitron to assume the role. { "Version": "2012-10-17", "Statement": [{ "Effect": "Allow", "Principal": { "Service": ["monitron.amazonaws.com"] }, "Action": "sts:AssumeRole" }] } Exporting your data with the console 344 Amazon Monitron User Guide Exporting your data with the console 345 Amazon Monitron 6. Choose Update Trust Policy. Step 5: Create the support case User Guide 1. From your AWS console, choose the question mark icon near the upper right corner of any page, then choose Support Center. 2. On the next page, choose Create case. Exporting your data with the console 346 Amazon Monitron User Guide 3. On the How can we help? page, do the following: a. Choose Account and billing support. b. Under Service, choose Account. c. Under Category, choose Compliance & Accreditations. d. Choose Severity, if that option is available to you based on your support subscription. e. Choose Next step: Additional information. 4. In Additional information do the following: a. Under Subject, enter Amazon Monitron data export request. b. In the Description field, enter: 1. your account ID 2. the region of the bucket you created Exporting your data with the console 347 Amazon Monitron User Guide 3. the ARN of the bucket you created (for example: "arn:aws:s3:::bucketname") 4. the ARN of the role you created (for example: "arn:aws:iam::273771705212:role/role- for-monitron") c. Choose Next step: Solve now or contact us. 5. In Solve now or contact us do the following: a. In Solve now, select Next. Exporting your data with the console 348 Amazon Monitron User Guide b. c. In Contact us, choose your Preferred contact language and preferred method of contact. Choose Submit. A confirmation screen with your case ID and details will be displayed. Exporting your data with the console 349 Amazon Monitron User Guide An AWS customer support specialist will get back to you as soon as possible. If there are any issues with the steps listed, the specialist may ask you for more information. If all the necessary information has been provided, the specialist will let you know as soon as your data has been copied to the Amazon S3 bucket that you created above. Exporting your data with CloudShell Topics • Step 1: Creating an Amazon S3 bucket (with AWS CloudShell) • Step 2: Granting Amazon Monitron access to your Amazon S3 bucket (with AWS CloudShell) • Step 3: Creating your support ticket Step 1: Creating an Amazon S3 bucket (with AWS CloudShell) 1. Log in to the AWS Console. 2. Open AWS CloudShell AWS CloudShell is a command-line environment that operates inside your browser. Inside AWS CloudShell, you can use the AWS Command Line Interface to launch and configure many AWS services. 3. In AWS CloudShell, enter the following command, where bucketname is the name of the bucket you are creating: $ aws s3api create-bucket --bucket bucketname --region us-east-1 This command creates an Amazon S3 bucket to store your raw data. You will be able to easily access your bucket from the console, and download your data at your convenience. For more information, see Creating, configuring, and working with Amazon S3 buckets. Important You are responsible for taking the appropriate steps to secure your data. We strongly recommend using server-side encryption and blocking public access to your bucket. Exporting your data with CloudShell 350 Amazon Monitron User Guide In the command above, the bucket is created in the US East (N. Virginia) Region. You can optionally specify a different Region in the request body. For more information, see Regions, Availability Zones, and Local Zones. You should see output that looks something like this: { "Location": "/bucketname" } 4. Identify the Amazon Resource Name (ARN) of the bucket you created, which will be: arn:aws:s3:::bucketname Step 2: Granting Amazon Monitron access to your Amazon S3 bucket (with AWS CloudShell) 1. Paste the code below into a text editor, and save it as: monitron-assumes-role.json. Do not use Microsoft Word, which will add extra characters. Use a simple text editor like Notepad or TextEdit. This policy gives Amazon Monitron permission to assume the role that will allow it to access your S3 bucket. For more information, see Policies and permissions in IAM. { "Version": "2012-10-17", "Statement": [{ "Effect": "Allow", "Principal": { "Service": |
AmazonMonitronUG-057 | AmazonMonitronUG.pdf | 57 | Identify the Amazon Resource Name (ARN) of the bucket you created, which will be: arn:aws:s3:::bucketname Step 2: Granting Amazon Monitron access to your Amazon S3 bucket (with AWS CloudShell) 1. Paste the code below into a text editor, and save it as: monitron-assumes-role.json. Do not use Microsoft Word, which will add extra characters. Use a simple text editor like Notepad or TextEdit. This policy gives Amazon Monitron permission to assume the role that will allow it to access your S3 bucket. For more information, see Policies and permissions in IAM. { "Version": "2012-10-17", "Statement": [{ "Effect": "Allow", "Principal": { "Service": ["monitron.amazonaws.com"] }, "Action": "sts:AssumeRole" }] } 2. Paste the text below into a text editor, and save it as: monitron-role-accesses-s3.json This policy will allow Amazon Monitron (using the role created above) to access your Amazon S3 bucket. Exporting your data with CloudShell 351 Amazon Monitron User Guide { "Statement": [ { "Action": [ "s3:GetBucketAcl", "s3:GetBucketLocation", "s3:ListBucket" ], "Effect": "Allow", "Resource": [ "arn:aws:s3:::bucketname" ] }, { "Action": [ "s3:PutObject", "s3:GetBucketAcl" ], "Effect": "Allow", "Resource": [ "arn:aws:s3:::bucketname/*" ] } ], "Version": "2012-10-17" } 3. In the text file you just created, replace every occurrence of bucketname with the name of your bucket. For example, if the name of your bucket is relentless, then your file will look like this: { "Statement": [ { "Action": [ "s3:GetBucketAcl", "s3:GetBucketLocation", "s3:ListBucket" ], "Effect": "Allow", Exporting your data with CloudShell 352 Amazon Monitron User Guide "Resource": [ "arn:aws:s3:::relentless" ] }, { "Action": [ "s3:PutObject", "s3:GetBucketAcl" ], "Effect": "Allow", "Resource": [ "arn:aws:s3:::relentless/*" ] } ], "Version": "2012-10-17" } 4. Upload both of the json files that you just created to CloudShell in the home directory. To upload a file, choose Actions from the upper right hand corner of the CloudShell console page, then choose Upload file. 5. Enter the following on the command line in CloudShell: aws iam create-role --role-name role-for-monitron --assume-role-policy-document "cat monitron-assumes-role.json" This command creates the role and attaches the monitron-assumes-role policy. You should see output that looks something like this: { "Role": { "Path": "/", "RoleName": "role-for-monitron", "RoleId": "AROAT7PQQWN6BMTMASVPP", "Arn": "arn:aws:iam::273771705212:role/role-for-monitron", "CreateDate": "2021-07-14T02:48:15+00:00", "AssumeRolePolicyDocument": { "Version": "2012-10-17", "Statement": [{ "Sid": "", "Effect": "Allow", Exporting your data with CloudShell 353 Amazon Monitron User Guide "Principal": { "Service": [ "monitron.amazonaws.com" ] }, "Action": "sts:AssumeRole" }] } } } Take note of the ARN value for the role you just created. You will need it later. In our example, the ARN value is: arn:aws:iam::273771705212:role/role-for- monitron 6. Enter the following on the command line in CloudShell: aws iam create-policy --policy-name role-uses-bucket --policy-document "cat role-uses- bucket.json" This command creates the monitron-role-accesses-s3 policy. You should see output that looks something like this: { "Policy": { "PolicyName": "role-uses-bucket", "PolicyId": "ANPAT7PQQWN6I5KLORSDQ", "Arn": "arn:aws:iam::273771705212:policy/role-uses-bucket", "Path": "/", "DefaultVersionId": "v1", "AttachmentCount": 0, "PermissionsBoundaryUsageCount": 0, "IsAttachable": true, "CreateDate": "2021-07-14T02:19:23+00:00", "UpdateDate": "2021-07-14T02:19:23+00:00" } } Take note of the ARN value for the policy that you just created. You will need it for the next step. Exporting your data with CloudShell 354 Amazon Monitron User Guide In our example, the ARN value is: arn:aws:iam::273771705212:policy/role-uses-bucket 7. Enter the following on the command line in CloudShell, replacing the ARN with the ARN for your role-uses-bucket policy: aws iam attach-role-policy --role-name role-for-monitron --policy-arn arn:aws:iam::273771705212:policy/role-uses-bucket This command attaches the monitron-role-accesses-s3 policy to the role you just created. Now you have created and provisioned an Amazon S3 bucket, a role that Amazon Monitron can assume, a policy that will allow Amazon Monitron to assume that role, and another policy that will allow the service using that role to use your Amazon S3 bucket. You are responsible for taking the appropriate steps to secure your data. We strongly recommend using server-side encryption and blocking public access to your bucket. For more information, see Blocking public access. Step 3: Creating your support ticket 1. From your AWS console, choose the question mark icon near the upper right corner of any page, then choose Support Center. Exporting your data with CloudShell 355 Amazon Monitron User Guide 2. On the next page, choose Create case. 3. On the How can we help? page, do the following: a. Choose Account and billing support. b. Under Service, choose Account. c. Under Category, choose Compliance & Accreditations. Exporting your data with CloudShell 356 Amazon Monitron User Guide d. Choose Severity, if that option is available to you based on your support subscription. e. Choose Next step: Additional information. 4. In Additional information do the following: a. Under Subject, enter Amazon Monitron data export request. b. In the Description field, enter: 1. your account ID 2. the region of the bucket you created 3. the ARN of the bucket you created (for example: "arn:aws:s3:::bucketname") 4. the ARN of the role you created (for example: "arn:aws:iam::273771705212:role/role- for-monitron") Exporting your data with CloudShell 357 Amazon Monitron User Guide c. Choose Next step: Solve |
AmazonMonitronUG-058 | AmazonMonitronUG.pdf | 58 | with CloudShell 356 Amazon Monitron User Guide d. Choose Severity, if that option is available to you based on your support subscription. e. Choose Next step: Additional information. 4. In Additional information do the following: a. Under Subject, enter Amazon Monitron data export request. b. In the Description field, enter: 1. your account ID 2. the region of the bucket you created 3. the ARN of the bucket you created (for example: "arn:aws:s3:::bucketname") 4. the ARN of the role you created (for example: "arn:aws:iam::273771705212:role/role- for-monitron") Exporting your data with CloudShell 357 Amazon Monitron User Guide c. Choose Next step: Solve now or contact us. 5. In Solve now or contact us do the following: a. In Solve now, select Next. Exporting your data with CloudShell 358 Amazon Monitron User Guide b. c. In Contact us, choose your Preferred contact language and preferred method of contact. Choose Submit. A confirmation screen with your case ID and details will be displayed. Exporting your data with CloudShell 359 Amazon Monitron User Guide An AWS customer support specialist will get back to you as soon as possible. If there are any issues with the steps listed, the specialist may ask you for more information. If all the necessary information has been provided, the specialist will let you know as soon as your data has been copied to the Amazon S3 bucket that you created above. Amazon Monitron Kinesis data export v1 Note Amazon Monitron Kinesis data export schema v1 has been deprecated. Learn more about the v2 data export schema. You can export incoming measurement data and the corresponding inference results from Amazon Monitron and perform real-time analysis. Data export streams live data to Kinesis. Topics • Exporting your data to a Kinesis stream • Editing live data export settings • Stopping a live data export • Viewing data export errors • Using server-side encryption for the Kinesis stream • Monitoring with Amazon CloudWatch Logs • Storing exported data in Amazon S3 • Processing data with Lambda • Understanding the v1 data export schema Exporting your data to a Kinesis stream 1. From your project's main page, near the bottom of the page, on the right, choose Start live data export. 2. Under Select Amazon Kinesis data stream, do one of the following: • Enter the name of an existing stream in the search box. Then skip to Step 5. Exporting your data with Kinesis v1 360 Amazon Monitron User Guide • Choose Create a new data stream. 3. On the Create data stream page, under Data stream configuration, enter your data stream name. 4. Under Data stream capacity, choose your capacity mode: • If your data stream’s throughput requirements are unpredictable and variable, choose On- demand. • If you can reliably estimate the throughput requirements of your data stream, choose Provisioned. Then, under provisioned shards, enter the number of shards you want to create, or choose the Shard estimator. 5. Choose Create data stream. Editing live data export settings To edit your live data export settings: 1. Open the Amazon Monitron console. 2. Choose Projects from the navigation pane. 3. If you have multiple projects, choose the project for which you want to edit the export settings. 4. From the main page for your project, under Live data export, from the Actions dropdown menu, choose Edit live data export settings. Stopping a live data export 1. Open the Amazon Monitron console. 2. Choose Projects from the navigation pane. 3. If you have multiple projects, choose the project for which you want to edit the export settings. 4. From the main page for your project, under Live data export, from the Actions dropdown menu, choose Stop live data export. 5. In the pop-up window, choose Stop. Editing live data export settings 361 Amazon Monitron User Guide Viewing data export errors To view the error messages in the CloudWatch Logs interface: • On the Amazon Monitron console, from the main page for your project, under Live data export, choose CloudWatch log group. Using server-side encryption for the Kinesis stream You can enable server-side encryption for your Kinesis stream before setting up Kinesis data export. However, if server-side encryption is enabled after Kinesis data export is set up, Amazon Monitron will not be able to publish to the stream. That's because Amazon Monitron will not have permission to call kms:GenerateDataKey so that it can encrypt data sent to Kinesis. To work around this, follow the instructions under ???, but without changing the configuration. This will associate the encryption you have set up with your export configuration. Monitoring with Amazon CloudWatch Logs You can monitor Amazon Monitron live data export using Amazon CloudWatch Logs. When a measurement fails to export, Amazon Monitron will send a log event to your CloudWatch Logs. You can also set up a |
AmazonMonitronUG-059 | AmazonMonitronUG.pdf | 59 | set up, Amazon Monitron will not be able to publish to the stream. That's because Amazon Monitron will not have permission to call kms:GenerateDataKey so that it can encrypt data sent to Kinesis. To work around this, follow the instructions under ???, but without changing the configuration. This will associate the encryption you have set up with your export configuration. Monitoring with Amazon CloudWatch Logs You can monitor Amazon Monitron live data export using Amazon CloudWatch Logs. When a measurement fails to export, Amazon Monitron will send a log event to your CloudWatch Logs. You can also set up a metric filter on the error log to generate metrics and set up alarms. An alarm can watch for certain thresholds and send notifications or take actions when those thresholds are met. For more information, see the CloudWatch User Guide. Amazon Monitron sends log events to the /aws/monitron/data-export/{HASH_ID} log group. The log event has the following JSON format: { "assetDisplayName": "string", "destination": "string", "errorCode": "string", "errorMessage": "string", "eventId": "string", "positionDisplayName": "string", "projectDisplayName": "string", "projectName": "string", "sensorId": "string", "siteDisplayName": "string", Viewing data export errors 362 User Guide Amazon Monitron "timestamp": "string" } assetDisplayName • The asset name displayed in the App • Type: String destination • The ARN of the Kinesis data stream • Type: String • Pattern: arn:aws:kinesis:{{REGION}}:{{AWS_ACCOUNT_ID}}:stream/{{STREAM_NAME}} errorCode • The error code • Type: String • Valid Values: INTERNAL_SEVER_ERROR | KINESIS_RESOURCE_NOT_FOUND | KINESIS_PROVISIONED_THROUGHPUT_EXCEEDED | KMS_ACCESS_DENIED | KMS_NOT_FOUND | KMS_DISABLED | KMS_INVALID_STATE | KMS_THROTTLING errorMessage • The detailed error message • Type: String eventId • The unique event ID corresponding to each measurement export • Type: String positionDisplayName • The sensor position name displayed in the App • Type: String sensorId • The physical ID of the sensor from which the measurement is sent • Type: String siteDisplayName • The site name displayed in the App Monitoring with Amazon CloudWatch Logs 363 Amazon Monitron • Type: String timestamp User Guide • The timestamp when the measurement is received by Amazon Monitron service in UTC • Type: String • Pattern: yyyy-mm-dd hh:mm:ss.SSS Storing exported data in Amazon S3 Topics • Using a predefined CloudFormation template • Configuring Kinesis manually in the console Using a predefined CloudFormation template Amazon Monitron provides a predefined AWS CloudFormation template to help quickly set up the Firehose to deliver data from a Kinesis data stream to the Amazon S3 bucket. This template enables dynamic partitioning and the Amazon S3 objects delivered will use the following key format recommended by Amazon Monitron: /project={projectName}/site={siteName}/ time={yyyy-mm-dd 00:00:00}/{filename} 1. Sign into your AWS account. 2. Open a new browser tab with the following URL: https://console.aws.amazon.com/cloudformation/home?region=us-east-1#/stacks/create/ review?templateURL=https://s3.us-east-1.amazonaws.com/monitron-cloudformation- templates-us-east-1/monitron_kinesis_data_export.yaml&stackName=monitron-kinesis- live-data-export 3. On the AWS CloudFormation page that opens, in the upper right corner, select the region in which you are using Amazon Monitron. 4. By default, the template will create a new Kinesis data stream and S3 bucket along with other resources needed to deliver data to Amazon S3. You can change the parameters to use existing resources. 5. Check the box that says I acknowledge that AWS CloudFormation might create IAM resources. 6. Choose Create stack. Storing exported data in Amazon S3 364 Amazon Monitron User Guide 7. On the next page, choose the refresh icon as often as you like until the status of the stack is CREATE_COMPLETE. Configuring Kinesis manually in the console 1. Sign in to the AWS Management Console and open the Kinesis console at https:// console.aws.amazon.com/kinesis. 2. Choose Delivery streams in the navigation pane. 3. Choose Create delivery stream. 4. 5. For Source, select Amazon Kinesis Data Streams. For Destination, select Amazon S3. 6. Under Source settings, Kinesis data stream, enter the ARN of your Kinesis data stream. 7. Under delivery stream name, enter the name of your Kinesis data stream. 8. Under Desination settings, choose an Amazon S3 bucket or enter a bucket URI. 9. (optional) Enable dynamic partitioning using inline parsing for JSON. This option is appropriate if you want to partition streaming measurement data based on source information and timestamp. For example: • Choose Enabled for Dynamic partitioning. • Choose Enabled for New line delimiter. • Choose Enabled for Inline parsing for JSON. • Under Dynamic partitioning keys, add: Key name project site time JQ expression .projectDisplayName| "project=\(.)" .siteDisplayName| "site=\(.)" .timestamp| sub("[0-9]{2}:[0-9]{2}:[0-9]{2}. [0-9]{3}$"; "00:00:00")| "time=\(.)" 10. Choose Apply dynamic partitioning keys and confirm the generated Amazon S3 bucket prefix is !{partitionKeyFromQuery:project}/!{partitionKeyFromQuery:site}/! {partitionKeyFromQuery:time}/. Storing exported data in Amazon S3 365 Amazon Monitron User Guide 11. In Amazon S3, objects will use the following key format: /project={projectName}/ site={siteName}/time={yyyy-mm-dd 00:00:00}/{filename}. 12. Choose Create delivery stream. 13. (optional) Use a more granular path. If you chose a dynamic partition, use the preceeding Amazon S3 key format if you plan to use AWS Glue and Athena to query the data. You can also choose a finer key format, but |
AmazonMonitronUG-060 | AmazonMonitronUG.pdf | 60 | JQ expression .projectDisplayName| "project=\(.)" .siteDisplayName| "site=\(.)" .timestamp| sub("[0-9]{2}:[0-9]{2}:[0-9]{2}. [0-9]{3}$"; "00:00:00")| "time=\(.)" 10. Choose Apply dynamic partitioning keys and confirm the generated Amazon S3 bucket prefix is !{partitionKeyFromQuery:project}/!{partitionKeyFromQuery:site}/! {partitionKeyFromQuery:time}/. Storing exported data in Amazon S3 365 Amazon Monitron User Guide 11. In Amazon S3, objects will use the following key format: /project={projectName}/ site={siteName}/time={yyyy-mm-dd 00:00:00}/{filename}. 12. Choose Create delivery stream. 13. (optional) Use a more granular path. If you chose a dynamic partition, use the preceeding Amazon S3 key format if you plan to use AWS Glue and Athena to query the data. You can also choose a finer key format, but the Amazon Athena query will not be efficient. Here is an example of setting up a finer Amazon S3 key path. Under Dynamic partitioning keys, add: Key name JQ expression project site asset position sensor date .projectDisplayName| "project=\(.)" .siteDisplayName| "site=\(.)" .assetDisplayName| "asset=\(.)" .sensorPositionDisplayName| "position=\(.)" .sensor.physicalId | "sensor=\(.)" .timestamp| sub(" [0-9]{2}:[0-9]{2}:[0-9]{2}. [0-9]{3}$"; "")| "date=\(.)" In Amazon S3, objects will use the following key format: /project={projectName}/ site={siteName}/asset={assetName}/position={positionName}/ sensor={sensorId}/date={yyyy-mm-dd}/time={HH:MM:SS}/{filename} Processing data with Lambda Topics • Step 1: Create the IAM role that gives your function permission to access AWS resources • Step 2: Create the Lambda function Processing data with Lambda 366 Amazon Monitron User Guide • Step 3: Configure the Lambda function • Step 4: Enable Kinesis trigger in AWS Lambda console Step 1: Create the IAM role that gives your function permission to access AWS resources 1. Open the roles page in the IAM console. 2. Choose Create role. 3. Create a role with the following properties. • Trusted entity: Lambda • Permissions: AWSLambdaKinesisExecutionRole (and AWSKeyManagementServicePowerUser if the Kinesis stream is encrypted) • Role name: lambda-kinesis-role Processing data with Lambda 367 Amazon Monitron User Guide Step 2: Create the Lambda function 1. Open the Functions page in the Lambda console. 2. Choose Create function. 3. Choose Use a blueprint. Processing data with Lambda 368 Amazon Monitron User Guide 4. In the Blueprints search bar, search and choose kinesis-process-record (nodejs) or kinesis- process-record-python. 5. Choose Configure. Step 3: Configure the Lambda function 1. Choose Function name 2. Choose the role created in the first step as the Execution role. 3. Configure Kinesis trigger. 1. Choose your Kinesis stream. 2. Click Create function. Processing data with Lambda 369 Amazon Monitron User Guide Processing data with Lambda 370 Amazon Monitron User Guide Step 4: Enable Kinesis trigger in AWS Lambda console 1. On the Configuration tab, choose Triggers. 2. Check the box next to the name of the Kinesis stream and choose Enable. The blueprint used in this example only consumes log data from the selected stream. You can further edit Lambda function code later to complete a more complicated task. Processing data with Lambda 371 Amazon Monitron User Guide Understanding the v1 data export schema Note Amazon Monitron Kinesis data export schema v1 has been deprecated. Learn more about the v2 data export schema. Each measurement data and its corresponding inference result are exported as one Kinesis data stream record in JSON format. Topics • v1 schema format • v1 schema parameters v1 schema format { "timestamp": "string", "eventId": "string", "version": "string", "projectDisplayName": "string", "siteDisplayName": "string", "assetDisplayName": "string", "sensorPositionDisplayName": "string", "sensor": { "physicalId": "string", "rssi": number }, "gateway": { "physicalId": "string" }, "measurement": { "features": { "acceleration": { "band0To6000Hz": { "xAxis": { "rms": number }, "yAxis": { Understanding the v1 data export schema 372 Amazon Monitron User Guide "rms": number }, "zAxis": { "rms": number } }, "band10To1000Hz": { "resultantVector": { "absMax": number, "absMin": number, "crestFactor": number, "rms": number }, "xAxis": { "rms": number }, "yAxis": { "rms": number }, "zAxis": { "rms": number } } }, "temperature": number, "velocity": { "band10To1000Hz": { "resultantVector": { "absMax": number, "absMin": number, "crestFactor": number, "rms": number }, "xAxis": { "rms": number }, "yAxis": { "rms": number }, "zAxis": { "rms": number } } } Understanding the v1 data export schema 373 User Guide Amazon Monitron }, "sequenceNo": number }, "models": { "temperatureML": { "persistentClassificationOutput": "string", "pointwiseClassificationOutput": "string" }, "vibrationISO": { "isoClass": "string", "mutedThreshold": "string", "persistentClassificationOutput": "string", "pointwiseClassificationOutput": "string" }, "vibrationML": { "persistentClassificationOutput": "string", "pointwiseClassificationOutput": "string" } }, "assetState": { "newState": "string", "previousState": "string" } } v1 schema parameters timestamp • The timestamp when the measurement is received by Monitron service in UTC • Type: String • Pattern: yyyy-mm-dd hh:mm:ss.SSS eventId • The unique data export event ID assigned for each measurement. Can be used to deduplicate the Kinesis stream records received. • Type: String version • Schema version • Type: String • Current Value: 1.0 Understanding the v1 data export schema 374 User Guide Amazon Monitron projectDisplayName • The project name displayed in the App and console • Type: String siteDisplayName • The site name displayed in the App • Type: String assetDisplayName • The asset name displayed in the App • Type: String sensorPositionDisplayName • |
AmazonMonitronUG-061 | AmazonMonitronUG.pdf | 61 | received by Monitron service in UTC • Type: String • Pattern: yyyy-mm-dd hh:mm:ss.SSS eventId • The unique data export event ID assigned for each measurement. Can be used to deduplicate the Kinesis stream records received. • Type: String version • Schema version • Type: String • Current Value: 1.0 Understanding the v1 data export schema 374 User Guide Amazon Monitron projectDisplayName • The project name displayed in the App and console • Type: String siteDisplayName • The site name displayed in the App • Type: String assetDisplayName • The asset name displayed in the App • Type: String sensorPositionDisplayName • The sensor position name displayed in the App • Type: String sensor.physicalId • The physical ID of the sensor from which the measurement is sent • Type: String sensor.rssi • The sensor bluetooth received signal strength indicator value • Type: Number • Unit: dBm gateway.physicalId • The physical ID of the gateway used to transmit data to Amazon Monitron service • Type: String measurement.features.acceleration.band0To6000Hz.xAxis.rms • The root mean square of the acceleration observed in the frequency band 0–6000 Hz in the x axis • Type: Number • Unit: m/s^2 measurement.features.acceleration.band0To6000Hz.yAxis.rms • The root mean square of the acceleration observed in the frequency band 0–6000 Hz in the y axis Understanding the v1 data export schema 375 Amazon Monitron • Type: Number • Unit: m/s^2 User Guide measurement.features.acceleration.band0To6000Hz.zAxis.rms • The root mean square of the acceleration observed in the frequency band 0–6000 Hz in the y axis • Type: Number • Unit: m/s^2 measurement.features.acceleration.band10To1000Hz.resultantVector.absMax • The absolute maximum acceleration observed in the frequency band 10–1000 Hz • Type: Number • Unit: m/s^2 measurement.features.acceleration.band10To1000Hz.resultantVector.absMin • The absolute minimum acceleration observed in the frequency band 10–1000 Hz • Type: Number • Unit: m/s^2 measurement.features.acceleration.band10To1000Hz.resultantVector.crestFactor • The acceleration crest factor observed in the frequency band 10–1000 Hz • Type: Number measurement.features.acceleration.band10To1000Hz.resultantVector.rms • The root mean square of the acceleration observed in the frequency band 10–1000 Hz • Type: Number • m/s^2 measurement.features.acceleration.band10To1000Hz.xAxis.rms • The root mean square of the acceleration observed in the frequency band 10–1000 Hz in the x axis • Type: Number • m/s^2 measurement.features.acceleration.band10To1000Hz.yAxis.rms • The root mean square of the acceleration observed in the frequency band 10–1000 Hz in the y axis • Type: Number Understanding the v1 data export schema 376 Amazon Monitron • m/s^2 User Guide measurement.features.acceleration.band10To1000Hz.zAxis.rms • The root mean square of the acceleration observed in the frequency band 10–1000 Hz in the z axis • Type: Number • m/s^2 measurement.features.temperature • The temperature observed • Type: Number • °C/degC measurement.features.velocity.band10To1000Hz.resultantVector.absMax • The absolute maximum velocity observed in the frequency band 10–1000 Hz • Type: Number • mm/s measurement.features.velocity.band10To1000Hz.resultantVector.absMin • The absolute minimum velocity observed in the frequency band 10–1000 Hz • Type: Number • mm/s measurement.features.velocity.band10To1000Hz.resultantVector.crestFactor • The velocity crest factor observed in the frequency band 10–1000 Hz • Type: Number measurement.features.velocity.band10To1000Hz.resultantVector.rms • The root mean square of the velocity observed in the frequency band 10–1000 Hz • Type: Number • mm/s measurement.features.velocity.band10To1000Hz.xAxis.rms • The root mean square of the velocity observed in the frequency band 10–1000 Hz in the x axis • Type: Number • mm/s Understanding the v1 data export schema 377 Amazon Monitron User Guide measurement.features.velocity.band10To1000Hz.yAxis.rms • The root mean square of the velocity observed in the frequency band 10–1000 Hz in the y axis • Type: Number • mm/s measurement.features.velocity.band10To1000Hz.zAxis.rms • The root mean square of the velocity observed in the frequency band 10–1000 Hz in the z axis • Type: Number • mm/s measurement.sequenceNo • The measurement sequence number • Type: Number models.temperatureML.persistentClassificationOutput • The persistent classification output from the machine learning based temperature model • Type: Number • Valid Values: UNKNOWN | HEALTHY | WARNING | ALARM models.temperatureML.pointwiseClassificationOutput • The point–wise classification output from the machine learning based temperature model • Type: String • Valid Values: UNKNOWN | INITIALIZING | HEALTHY | WARNING | ALARM models.vibrationISO.isoClass • The ISO 20816 class (a standard for measurement and evaluation of machine vibration) used by the ISO based vibration model • Type: String • Valid Values: CLASS1 | CLASS2 | CLASS3 | CLASS4 | FAN_BV2 models.vibrationISO.mutedThreshold • The threshold to mute the notification from the ISO based vibration model • Type: String • Valid Values: WARNING | ALARM Understanding the v1 data export schema 378 Amazon Monitron User Guide models.vibrationISO.persistentClassificationOutput • The persistent classification output from the ISO based vibration model • Type: String • Valid Values: UNKNOWN | HEALTHY | WARNING | ALARM models.vibrationISO.pointwiseClassificationOutput • The point–wise classification output from the the ISO based vibration model • Type: String • Valid Values: UNKNOWN | HEALTHY | WARNING | ALARM | MUTED_WARNING | MUTED_ALARM models.vibrationML.persistentClassificationOutput • The persistent classification output from the machine learning based vibration model • Type: String • Valid Values: UNKNOWN | HEALTHY | WARNING | ALARM models.vibrationML.pointwiseClassificationOutput • The |
AmazonMonitronUG-062 | AmazonMonitronUG.pdf | 62 | Valid Values: WARNING | ALARM Understanding the v1 data export schema 378 Amazon Monitron User Guide models.vibrationISO.persistentClassificationOutput • The persistent classification output from the ISO based vibration model • Type: String • Valid Values: UNKNOWN | HEALTHY | WARNING | ALARM models.vibrationISO.pointwiseClassificationOutput • The point–wise classification output from the the ISO based vibration model • Type: String • Valid Values: UNKNOWN | HEALTHY | WARNING | ALARM | MUTED_WARNING | MUTED_ALARM models.vibrationML.persistentClassificationOutput • The persistent classification output from the machine learning based vibration model • Type: String • Valid Values: UNKNOWN | HEALTHY | WARNING | ALARM models.vibrationML.pointwiseClassificationOutput • The point–wise classification output from the machine learning based vibration model • Type: String • Valid Values: UNKNOWN | INITIALIZING | HEALTHY | WARNING | ALARM assetState.newState • The machine status after processing the measurement • Type: String • Valid Values: UNKNOWN | HEALTHY | NEEDS_MAINTENANCE | WARNING | ALARM assetState.previousState • The machine status before processing the measurement • Type: String • Valid Values: UNKNOWN | HEALTHY | NEEDS_MAINTENANCE | WARNING | ALARM Amazon Monitron Kinesis data export v2 You can export incoming measurement data and the corresponding inference results from Amazon Monitron and perform real-time analysis. Data export streams live data to Kinesis. Topics Exporting your data with Kinesis v2 379 Amazon Monitron User Guide • Exporting your data to a Kinesis stream • Editing live data export settings • Stopping a live data export • Viewing data export errors • Using server-side encryption for the Kinesis stream • Monitoring with Amazon CloudWatch Logs • Storing exported data in Amazon S3 • Processing data with Lambda • Understanding the v2 data export schema • Migration from Kinesis v1 to v2 Exporting your data to a Kinesis stream 1. From your project's main page, near the bottom of the page, on the right, choose Start live data export. 2. Under Select Kinesis data stream, do one of the following: • Enter the name of an existing stream in the search box. Then skip to Step 5. • Choose Create a new data stream. 3. On the Create data stream page, under Data stream configuration, enter your data stream name. 4. Under Data stream capacity, choose your capacity mode: • If your data stream’s throughput requirements are unpredictable and variable, choose On- demand. • If you can reliably estimate the throughput requirements of your data stream, choose Provisioned. Then, under provisioned shards, enter the number of shards you want to create, or choose the Shard estimator. 5. Choose Create data stream. Editing live data export settings To edit your live data export settings: Exporting your data to a Kinesis stream 380 Amazon Monitron User Guide 1. Open the Amazon Monitron console. 2. Choose Projects from the navigation pane. 3. If you have multiple projects, choose the project for which you want to edit the export settings. 4. From the main page for your project, under Live data export, from the Actions dropdown menu, choose Edit live data export settings. Stopping a live data export 1. Open the Amazon Monitron console. 2. Choose Projects from the navigation pane. 3. 4. If you have multiple projects, choose the project for which you want to edit the export settings. From the main page for your project, under Live data export, from the Actions dropdown menu, choose Stop live data export. 5. In the pop-up window, choose Stop. Viewing data export errors To view the error messages in the CloudWatch Logs interface: • On the Amazon Monitron console, from the main page for your project, under Live data export, choose CloudWatch log group. Using server-side encryption for the Kinesis stream You can enable server-side encryption for your Kinesis stream before setting up Kinesis data export. However, if server-side encryption is enabled after Kinesis data export is set up, Amazon Monitron will not be able to publish to the stream. That's because Amazon Monitron will not have permission to call kms:GenerateDataKey so that it can encrypt data sent to Kinesis. To work around this, follow the instructions under ???, but without changing the configuration. This will associate the encryption you have set up with your export configuration. Stopping a live data export 381 Amazon Monitron User Guide Monitoring with Amazon CloudWatch Logs You can monitor Amazon Monitron live data export using Amazon CloudWatch Logs. When a measurement fails to export, Amazon Monitron will send a log event to your CloudWatch Logs. You can also set up a metric filter on the error log to generate metrics and set up alarms. An alarm can watch for certain thresholds and send notifications or take actions when those thresholds are met. For more information, see the CloudWatch User Guide. Amazon Monitron sends log events to the /aws/monitron/data-export/{HASH_ID} log group. The log event has the following JSON format: { "assetName": |
AmazonMonitronUG-063 | AmazonMonitronUG.pdf | 63 | Guide Monitoring with Amazon CloudWatch Logs You can monitor Amazon Monitron live data export using Amazon CloudWatch Logs. When a measurement fails to export, Amazon Monitron will send a log event to your CloudWatch Logs. You can also set up a metric filter on the error log to generate metrics and set up alarms. An alarm can watch for certain thresholds and send notifications or take actions when those thresholds are met. For more information, see the CloudWatch User Guide. Amazon Monitron sends log events to the /aws/monitron/data-export/{HASH_ID} log group. The log event has the following JSON format: { "assetName": "string", "destination": "string", "errorCode": "string", "errorMessage": "string", "eventId": "string", "eventType": "string", "positionName": "string", "projectName": "string", "projectId": "string", "sensorId": "string", "gatewayId": "string", "siteName": "string", "timestamp": "string" } assetName • The asset name displayed in the app • Type: String destination • The ARN of the Kinesis data stream • Type: String • Pattern: arn:aws:kinesis:{{REGION}}:{{AWS_ACCOUNT_ID}}:stream/{{STREAM_NAME}} errorCode • The error code • Type: String Monitoring with Amazon CloudWatch Logs 382 Amazon Monitron User Guide • Valid Values: INTERNAL_SEVER_ERROR | KINESIS_RESOURCE_NOT_FOUND | KINESIS_PROVISIONED_THROUGHPUT_EXCEEDED | KMS_ACCESS_DENIED | KMS_NOT_FOUND | KMS_DISABLED | KMS_INVALID_STATE | KMS_THROTTLING errorMessage • The detailed error message • Type: String eventId • The unique event ID corresponding to each measurement export • Type: String eventType • The current event type • Type: String • Valid values: measurement | gatewayConnected | gatewayDisconnected | sensorConnected | sensorDisconnected | assetStateTransition positionName • The sensor position name displayed in the app • Type: String projectName • The project name displayed in the app and console • Type: String projectID • The unique project ID corresponding to the Amazon Monitron project • Type: String sensorID • The physical ID of the sensor from which the measurement is sent • Type: String gatewayID • The physical ID of the gateway used to transmit data to the Amazon Monitron service • Type: String Monitoring with Amazon CloudWatch Logs 383 Amazon Monitron siteName • The site name displayed in the app • Type: String timestamp User Guide • The timestamp when the measurement is received by the Amazon Monitron service in UTC • Type: String • Pattern: yyyy-mm-dd hh:mm:ss.SSS Storing exported data in Amazon S3 If you want to store your exported data in Amazon S3, use the following procedure. Topics • Configuring Kinesis manually in the console Configuring Kinesis manually in the console 1. Sign in to the AWS Management Console and open the Kinesis console at https:// console.aws.amazon.com/kinesis. 2. Choose Delivery streams in the navigation pane. 3. Choose Create delivery stream. 4. 5. For Source, select Amazon Kinesis Data Streams. For Destination, select Amazon S3. 6. Under Source settings, Kinesis data stream, enter the ARN of your Kinesis data stream. 7. Under delivery stream name, enter the name of your Kinesis data stream. 8. Under Desination settings, choose an Amazon S3 bucket or enter a bucket URI. 9. (optional) Enable dynamic partitioning using inline parsing for JSON. This option is appropriate if you want to partition streaming measurement data based on source information and timestamp. For example: • Choose Enabled for Dynamic partitioning. • Choose Enabled for New line delimiter. • Choose Enabled for Inline parsing for JSON. Storing exported data in Amazon S3 384 Amazon Monitron User Guide • Under Dynamic partitioning keys, add: Key name project site time JQ expression .projectName| "project=\(.)" .eventPayload.siteName| "site=\(.)" .timestamp| sub("[0-9]{2}:[0-9]{2}:[0-9]{2}. [0-9]{3}$"; "00:00:00")| "time=\(.)" 10. Choose Apply dynamic partitioning keys and confirm the generated Amazon S3 bucket prefix is !{partitionKeyFromQuery:project}/!{partitionKeyFromQuery:site}/! {partitionKeyFromQuery:time}/. 11. In Amazon S3, objects will use the following key format: /project={projectName}/ site={siteName}/time={yyyy-mm-dd 00:00:00}/{filename}. 12. Choose Create delivery stream. Processing data with Lambda Topics • Step 1: Create the IAM role that gives your function permission to access AWS resources • Step 2: Create the Lambda function • Step 3: Configure the Lambda function • Step 4: Enable Kinesis trigger in AWS Lambda console Step 1: Create the IAM role that gives your function permission to access AWS resources 1. Open the roles page in the IAM console. 2. Choose Create role. 3. On the Select trusted entity page, do the following: • In Trusted entity type, choose AWS service. • In Use case, for Service or use case choose Lambda. Processing data with Lambda 385 Amazon Monitron • Choose Next. User Guide 4. In the Add permissions page, do the following: • In Permissions policies, choose AWSLambdaKinesisExecutionRole (and AWSKeyManagementServicePowerUser if the Kinesis stream is encrypted). • Leave the configurations in Set permissions boundary as is. • Choose Next. Processing data with Lambda 386 Amazon Monitron User Guide 5. In the Name, review, and create page, do the following: • In Role details, for Role name, enter a name for your role. For example lambda-kinesis- role. You can also choose to add an optional Description. • Leave the settings for |
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