Module Awso_machinelearning_lwtSource

The name of a database hosted on an RDS DB instance.

Identifier of RDS DB Instances.

The ID of an Amazon Redshift cluster.

The name of a database hosted on an Amazon Redshift cluster.

A timestamp represented in epoch time.

Integer type that is a 32-bit signed number.

String type.

The database details of an Amazon RDS database.

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.

The SQL query to be executed against the Amazon RDS database. The SQL query should be valid for the Amazon RDS type being used.

Describes the database details required to connect to an Amazon Redshift database.

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.

Describes the SQL query to execute on the Amazon Redshift database. The SQL query should be valid for an Amazon Redshift SELECT.

An Amazon Web Service (AWS) user account identifier. The account identifier can be an AWS root account or an AWS Identity and Access Management (IAM) user.

Long integer type that is a 64-bit signed number.

Description of the most recent details about an object.

Describes the real-time endpoint information for an MLModel.

A reference to a file or bucket on Amazon Simple Storage Service (Amazon S3).

A user-supplied name or description of the Amazon ML resource.

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.

The datasource details that are specific to Amazon RDS.

Describes the DataSource details specific to Amazon Redshift.

The Amazon Resource Name (ARN) of an AWS IAM Role, such as the following: arn:aws:iam::account:role/rolename.

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.

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.

The name of a variable. Currently it's used to specify the name of the target value, label, weight, and tags.

The value of a variable. Currently it's used to specify values of the target value, weights, and tag variables and for filtering variable values.

A custom key-value pair associated with an ML object, such as an ML model.

Represents the output of a GetMLModel operation. The content consists of the detailed metadata and the current status of the MLModel.

Represents the output of GetEvaluation operation. The content consists of the detailed metadata and data file information and the current status of the Evaluation.

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.

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.

The schema of a DataSource. The DataSchema defines the structure of the observation data in the data file(s) referenced in the DataSource. The DataSource schema is expressed in JSON format. DataSchema is not required if you specify a DataSchemaUri { "version": "1.0", "recordAnnotationFieldName": "F1", "recordWeightFieldName": "F2", "targetFieldName": "F3", "dataFormat": "CSV", "dataFileContainsHeader": true, "variables": [ { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ], "excludedVariableNames": [ "F6" ] }

Describes the database credentials for connecting to a database on an Amazon Redshift cluster.

The database credentials to connect to a database on an RDS DB instance.

An error on the server occurred when trying to process a request.

An error on the client occurred. Typically, the cause is an invalid input value.

A specified resource cannot be located.

The subscriber exceeded the maximum number of operations. This exception can occur when listing objects such as DataSource.

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.

The exception is thrown when a predict request is made to an unmounted MLModel.

Specifies whether a describe operation should return exhaustive or abbreviated information.

The value specified in a filtering condition. The ComparatorValue becomes the reference value when matching or evaluating data values in filtering and searching functions.

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.

Describes the data specification of a DataSource.

Describes the data specification of an Amazon Redshift DataSource.

The data specification of an Amazon Relational Database Service (Amazon RDS) DataSource.

Represents the output of an UpdateMLModel operation. You can see the updated content by using the GetMLModel operation.

Updates the MLModelName and the ScoreThreshold of an MLModel. You can use the GetMLModel operation to view the contents of the updated data element.

Represents the output of an UpdateEvaluation operation. You can see the updated content by using the GetEvaluation operation.

Updates the EvaluationName of an Evaluation. You can use the GetEvaluation operation to view the contents of the updated data element.

Represents the output of an UpdateDataSource operation. You can see the updated content by using the GetBatchPrediction operation.

Updates the DataSourceName of a DataSource. You can use the GetDataSource operation to view the contents of the updated data element.

Represents the output of an UpdateBatchPrediction operation. You can see the updated content by using the GetBatchPrediction operation.

Updates the BatchPredictionName of a BatchPrediction. You can use the GetBatchPrediction operation to view the contents of the updated data element.

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.

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.

Represents the output of a GetMLModel operation, and provides detailed information about a MLModel.

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.

Represents the output of a GetEvaluation operation and describes an Evaluation.

Returns an Evaluation that includes metadata as well as the current status of the Evaluation.

Represents the output of a GetDataSource operation and describes a DataSource.

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.

Represents the output of a GetBatchPrediction operation and describes a BatchPrediction.

Returns a BatchPrediction that includes detailed metadata, status, and data file information for a Batch Prediction request.

Amazon ML returns the following elements.

Describes one or more of the tags for your Amazon ML object.

Represents the output of a DescribeMLModels operation. The content is essentially a list of MLModel.

Returns a list of MLModel that match the search criteria in the request.

Represents the query results from a DescribeEvaluations operation. The content is essentially a list of Evaluation.

Returns a list of DescribeEvaluations that match the search criteria in the request.

Represents the query results from a DescribeDataSources operation. The content is essentially a list of DataSource.

Returns a list of DataSource that match the search criteria in the request.

Represents the output of a DescribeBatchPredictions operation. The content is essentially a list of BatchPredictions.

Returns a list of BatchPrediction operations that match the search criteria in the request.

Amazon ML returns the following elements.

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.

Represents the output of an DeleteRealtimeEndpoint operation. The result contains the MLModelId and the endpoint information for the MLModel.

Deletes a real time endpoint of an MLModel.

Represents the output of a DeleteMLModel operation. You can use the GetMLModel operation and check the value of the Status parameter to see whether an MLModel is marked as DELETED.

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.

Represents the output of a DeleteEvaluation operation. The output indicates that Amazon Machine Learning (Amazon ML) received the request. You can use the GetEvaluation operation and check the value of the Status parameter to see whether an Evaluation is marked as DELETED.

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.

Represents the output of a DeleteDataSource operation.

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.

Represents the output of a DeleteBatchPrediction operation. You can use the GetBatchPrediction operation and check the value of the Status parameter to see whether a BatchPrediction is marked as DELETED.

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.

Represents the output of an CreateRealtimeEndpoint operation. The result contains the MLModelId and the endpoint information for the MLModel. Note: The endpoint information includes the URI of the MLModel; that is, the location to send online prediction requests for the specified MLModel.

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.

Represents the output of a CreateMLModel operation, and is an acknowledgement that Amazon ML received the request. The CreateMLModel operation is asynchronous. You can poll for status updates by using the GetMLModel operation and checking the Status parameter.

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.

Represents the output of a CreateEvaluation operation, and is an acknowledgement that Amazon ML received the request. CreateEvaluation operation is asynchronous. You can poll for status updates by using the GetEvcaluation operation and checking the Status parameter.

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.

Represents the output of a CreateDataSourceFromS3 operation, and is an acknowledgement that Amazon ML received the request. The CreateDataSourceFromS3 operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status parameter.

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.

Represents the output of a CreateDataSourceFromRedshift operation, and is an acknowledgement that Amazon ML received the request. The CreateDataSourceFromRedshift operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status parameter.

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.

Represents the output of a CreateDataSourceFromRDS operation, and is an acknowledgement that Amazon ML received the request. The CreateDataSourceFromRDS> operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status parameter. You can inspect the Message when Status shows up as FAILED. You can also check the progress of the copy operation by going to the DataPipeline console and looking up the pipeline using the pipelineId from the describe call.

Creates a DataSource object from an Amazon Relational Database Service (Amazon RDS). A DataSource references data that can be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations. CreateDataSourceFromRDS is an asynchronous operation. In response to CreateDataSourceFromRDS, 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 the COMPLETED or PENDING state can be used only to perform >CreateMLModel>, CreateEvaluation, or CreateBatchPrediction operations. If Amazon ML cannot 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.

Represents the output of a CreateBatchPrediction operation, and is an acknowledgement that Amazon ML received the request. The CreateBatchPrediction operation is asynchronous. You can poll for status updates by using the >GetBatchPrediction operation and checking the Status parameter of the result.

Generates predictions for a group of observations. The observations to process exist in one or more data files referenced by a DataSource. This operation creates a new BatchPrediction, and uses an MLModel and the data files referenced by the DataSource as information sources. CreateBatchPrediction is an asynchronous operation. In response to CreateBatchPrediction, Amazon Machine Learning (Amazon ML) immediately returns and sets the BatchPrediction status to PENDING. After the BatchPrediction completes, Amazon ML sets the status to COMPLETED. You can poll for status updates by using the GetBatchPrediction operation and checking the Status parameter of the result. After the COMPLETED status appears, the results are available in the location specified by the OutputUri parameter.

Amazon ML returns the following elements.

Adds one or more tags to an object, up to a limit of 10. Each tag consists of a key and an optional value. If you add a tag using a key that is already associated with the ML object, AddTags updates the tag's value.