Class: AWS.MachineLearning

var params = {
  EvaluationDataSourceId: 'STRING_VALUE', /* required */
  EvaluationId: 'STRING_VALUE', /* required */
  MLModelId: 'STRING_VALUE', /* required */
  EvaluationName: 'STRING_VALUE'
};
machinelearning.createEvaluation(params, function(err, data) {
  if (err) console.log(err, err.stack); // an error occurred
  else     console.log(data);           // successful response
});

Parameters:

• params (Object) (defaults to: {}) —
  • EvaluationId — (String)

    A user-supplied ID that uniquely identifies the Evaluation.

  • EvaluationName — (String)

    A user-supplied name or description of the Evaluation.

  • MLModelId — (String)

    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.

  • EvaluationDataSourceId — (String)

    The ID of the DataSource for the evaluation. The schema of the DataSource must match the schema used to create the MLModel.

Callback (callback):

• function(err, data) { ... }

  Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.

  Context (this):

  • (AWS.Response) —

    the response object containing error, data properties, and the original request object.

  Parameters:

  • err (Error) —

    the error object returned from the request. Set to null if the request is successful.

  • data (Object) —

    the de-serialized data returned from the request. Set to null if a request error occurs. The data object has the following properties:

    • EvaluationId — (String)

      The user-supplied ID that uniquely identifies the Evaluation. This value should be identical to the value of the EvaluationId in the request.

Returns:

• (AWS.Request) —

  a handle to the operation request for subsequent event callback registration.

var params = {
  MLModelId: 'STRING_VALUE', /* required */
  MLModelType: REGRESSION | BINARY | MULTICLASS, /* required */
  TrainingDataSourceId: 'STRING_VALUE', /* required */
  MLModelName: 'STRING_VALUE',
  Parameters: {
    '<StringType>': 'STRING_VALUE',
    /* '<StringType>': ... */
  },
  Recipe: 'STRING_VALUE',
  RecipeUri: 'STRING_VALUE'
};
machinelearning.createMLModel(params, function(err, data) {
  if (err) console.log(err, err.stack); // an error occurred
  else     console.log(data);           // successful response
});

Parameters:

• params (Object) (defaults to: {}) —
  • MLModelId — (String)

    A user-supplied ID that uniquely identifies the MLModel.

  • MLModelName — (String)

    A user-supplied name or description of the MLModel.

  • MLModelType — (String)

    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.

    Possible values include:
    • "REGRESSION"
    • "BINARY"
    • "MULTICLASS"
  • Parameters — (map<String>)

    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 10000. 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.

  • TrainingDataSourceId — (String)

    The DataSource that points to the training data.

  • Recipe — (String)

    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.

  • RecipeUri — (String)

    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.

Callback (callback):

• function(err, data) { ... }

  Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.

  Context (this):

  • (AWS.Response) —

    the response object containing error, data properties, and the original request object.

  Parameters:

  • err (Error) —

    the error object returned from the request. Set to null if the request is successful.

  • data (Object) —

    the de-serialized data returned from the request. Set to null if a request error occurs. The data object has the following properties:

    • MLModelId — (String)

      A user-supplied ID that uniquely identifies the MLModel. This value should be identical to the value of the MLModelId in the request.

Returns:

• (AWS.Request) —

  a handle to the operation request for subsequent event callback registration.

var params = {
  MLModelId: 'STRING_VALUE' /* required */
};
machinelearning.createRealtimeEndpoint(params, function(err, data) {
  if (err) console.log(err, err.stack); // an error occurred
  else     console.log(data);           // successful response
});

Parameters:

• params (Object) (defaults to: {}) —
  • MLModelId — (String)

    The ID assigned to the MLModel during creation.

Callback (callback):

• function(err, data) { ... }

  Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.

  Context (this):

  • (AWS.Response) —

    the response object containing error, data properties, and the original request object.

  Parameters:

  • err (Error) —

    the error object returned from the request. Set to null if the request is successful.

  • data (Object) —

    the de-serialized data returned from the request. Set to null if a request error occurs. The data object has the following properties:

    • MLModelId — (String)

      A user-supplied ID that uniquely identifies the MLModel. This value should be identical to the value of the MLModelId in the request.

    • RealtimeEndpointInfo — (map)

      The endpoint information of the MLModel

      • PeakRequestsPerSecond — (Integer)

        The maximum processing rate for the real-time endpoint for MLModel, measured in incoming requests per second.

      • CreatedAt — (Date)

        The time that the request to create the real-time endpoint for the MLModel was received. The time is expressed in epoch time.

      • EndpointUrl — (String)

        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.

      • EndpointStatus — (String)

        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.

        Possible values include:
        • "NONE"
        • "READY"
        • "UPDATING"
        • "FAILED"

Returns:

• (AWS.Request) —

  a handle to the operation request for subsequent event callback registration.

var params = {
  BatchPredictionId: 'STRING_VALUE' /* required */
};
machinelearning.deleteBatchPrediction(params, function(err, data) {
  if (err) console.log(err, err.stack); // an error occurred
  else     console.log(data);           // successful response
});

Parameters:

• params (Object) (defaults to: {}) —
  • BatchPredictionId — (String)

    A user-supplied ID that uniquely identifies the BatchPrediction.

Callback (callback):

• function(err, data) { ... }

  Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.

  Context (this):

  • (AWS.Response) —

    the response object containing error, data properties, and the original request object.

  Parameters:

  • err (Error) —

    the error object returned from the request. Set to null if the request is successful.

  • data (Object) —

    the de-serialized data returned from the request. Set to null if a request error occurs. The data object has the following properties:

    • BatchPredictionId — (String)

      A user-supplied ID that uniquely identifies the BatchPrediction. This value should be identical to the value of the BatchPredictionID in the request.

Returns:

• (AWS.Request) —

  a handle to the operation request for subsequent event callback registration.

var params = {
  DataSourceId: 'STRING_VALUE' /* required */
};
machinelearning.deleteDataSource(params, function(err, data) {
  if (err) console.log(err, err.stack); // an error occurred
  else     console.log(data);           // successful response
});

Parameters:

• params (Object) (defaults to: {}) —
  • DataSourceId — (String)

    A user-supplied ID that uniquely identifies the DataSource.

Callback (callback):

• function(err, data) { ... }

  Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.

  Context (this):

  • (AWS.Response) —

    the response object containing error, data properties, and the original request object.

  Parameters:

  • err (Error) —

    the error object returned from the request. Set to null if the request is successful.

  • data (Object) —

    the de-serialized data returned from the request. Set to null if a request error occurs. The data object has the following properties:

    • DataSourceId — (String)

      A user-supplied ID that uniquely identifies the DataSource. This value should be identical to the value of the DataSourceID in the request.

Returns:

• (AWS.Request) —

  a handle to the operation request for subsequent event callback registration.

var params = {
  EvaluationId: 'STRING_VALUE' /* required */
};
machinelearning.deleteEvaluation(params, function(err, data) {
  if (err) console.log(err, err.stack); // an error occurred
  else     console.log(data);           // successful response
});

Parameters:

• params (Object) (defaults to: {}) —
  • EvaluationId — (String)

    A user-supplied ID that uniquely identifies the Evaluation to delete.

Callback (callback):

• function(err, data) { ... }

  Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.

  Context (this):

  • (AWS.Response) —

    the response object containing error, data properties, and the original request object.

  Parameters:

  • err (Error) —

    the error object returned from the request. Set to null if the request is successful.

  • data (Object) —

    the de-serialized data returned from the request. Set to null if a request error occurs. The data object has the following properties:

    • EvaluationId — (String)

      A user-supplied ID that uniquely identifies the Evaluation. This value should be identical to the value of the EvaluationId in the request.

Returns:

• (AWS.Request) —

  a handle to the operation request for subsequent event callback registration.

var params = {
  MLModelId: 'STRING_VALUE' /* required */
};
machinelearning.deleteMLModel(params, function(err, data) {
  if (err) console.log(err, err.stack); // an error occurred
  else     console.log(data);           // successful response
});

Parameters:

• params (Object) (defaults to: {}) —
  • MLModelId — (String)

    A user-supplied ID that uniquely identifies the MLModel.

Callback (callback):

• function(err, data) { ... }

  Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.

  Context (this):

  • (AWS.Response) —

    the response object containing error, data properties, and the original request object.

  Parameters:

  • err (Error) —

    the error object returned from the request. Set to null if the request is successful.

  • data (Object) —

    the de-serialized data returned from the request. Set to null if a request error occurs. The data object has the following properties:

    • MLModelId — (String)

      A user-supplied ID that uniquely identifies the MLModel. This value should be identical to the value of the MLModelID in the request.

Returns:

• (AWS.Request) —

  a handle to the operation request for subsequent event callback registration.

var params = {
  MLModelId: 'STRING_VALUE' /* required */
};
machinelearning.deleteRealtimeEndpoint(params, function(err, data) {
  if (err) console.log(err, err.stack); // an error occurred
  else     console.log(data);           // successful response
});

Parameters:

• params (Object) (defaults to: {}) —
  • MLModelId — (String)

    The ID assigned to the MLModel during creation.

Callback (callback):

• function(err, data) { ... }

  Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.

  Context (this):

  • (AWS.Response) —

    the response object containing error, data properties, and the original request object.

  Parameters:

  • err (Error) —

    the error object returned from the request. Set to null if the request is successful.

  • data (Object) —

    the de-serialized data returned from the request. Set to null if a request error occurs. The data object has the following properties:

    • MLModelId — (String)

      A user-supplied ID that uniquely identifies the MLModel. This value should be identical to the value of the MLModelId in the request.

    • RealtimeEndpointInfo — (map)

      The endpoint information of the MLModel

      • PeakRequestsPerSecond — (Integer)

        The maximum processing rate for the real-time endpoint for MLModel, measured in incoming requests per second.

      • CreatedAt — (Date)

        The time that the request to create the real-time endpoint for the MLModel was received. The time is expressed in epoch time.

      • EndpointUrl — (String)

        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.

      • EndpointStatus — (String)

        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.

        Possible values include:
        • "NONE"
        • "READY"
        • "UPDATING"
        • "FAILED"

Returns:

• (AWS.Request) —

  a handle to the operation request for subsequent event callback registration.

var params = {
  ResourceId: 'STRING_VALUE', /* required */
  ResourceType: BatchPrediction | DataSource | Evaluation | MLModel, /* required */
  TagKeys: [ /* required */
    'STRING_VALUE',
    /* more items */
  ]
};
machinelearning.deleteTags(params, function(err, data) {
  if (err) console.log(err, err.stack); // an error occurred
  else     console.log(data);           // successful response
});

Parameters:

• params (Object) (defaults to: {}) —
  • TagKeys — (Array<String>)

    One or more tags to delete.

  • ResourceId — (String)

    The ID of the tagged ML object. For example, exampleModelId.

  • ResourceType — (String)

    The type of the tagged ML object.

    Possible values include:
    • "BatchPrediction"
    • "DataSource"
    • "Evaluation"
    • "MLModel"

Callback (callback):

• function(err, data) { ... }

  Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.

  Context (this):

  • (AWS.Response) —

    the response object containing error, data properties, and the original request object.

  Parameters:

  • err (Error) —

    the error object returned from the request. Set to null if the request is successful.

  • data (Object) —

    the de-serialized data returned from the request. Set to null if a request error occurs. The data object has the following properties:

    • ResourceId — (String)

      The ID of the ML object from which tags were deleted.

    • ResourceType — (String)

      The type of the ML object from which tags were deleted.

      Possible values include:
      • "BatchPrediction"
      • "DataSource"
      • "Evaluation"
      • "MLModel"

Returns:

• (AWS.Request) —

  a handle to the operation request for subsequent event callback registration.

var params = {
  EQ: 'STRING_VALUE',
  FilterVariable: CreatedAt | LastUpdatedAt | Status | Name | IAMUser | MLModelId | DataSourceId | DataURI,
  GE: 'STRING_VALUE',
  GT: 'STRING_VALUE',
  LE: 'STRING_VALUE',
  LT: 'STRING_VALUE',
  Limit: 'NUMBER_VALUE',
  NE: 'STRING_VALUE',
  NextToken: 'STRING_VALUE',
  Prefix: 'STRING_VALUE',
  SortOrder: asc | dsc
};
machinelearning.describeBatchPredictions(params, function(err, data) {
  if (err) console.log(err, err.stack); // an error occurred
  else     console.log(data);           // successful response
});

Parameters:

• params (Object) (defaults to: {}) —
  • FilterVariable — (String)

    Use one of the following variables to filter a list of BatchPrediction:

    • 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.

    Possible values include:
    • "CreatedAt"
    • "LastUpdatedAt"
    • "Status"
    • "Name"
    • "IAMUser"
    • "MLModelId"
    • "DataSourceId"
    • "DataURI"
  • EQ — (String)

    The equal to operator. The BatchPrediction results will have FilterVariable values that exactly match the value specified with EQ.

  • GT — (String)

    The greater than operator. The BatchPrediction results will have FilterVariable values that are greater than the value specified with GT.

  • LT — (String)

    The less than operator. The BatchPrediction results will have FilterVariable values that are less than the value specified with LT.

  • GE — (String)

    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.

  • LE — (String)

    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.

  • NE — (String)

    The not equal to operator. The BatchPrediction results will have FilterVariable values not equal to the value specified with NE.

  • Prefix — (String)

    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

  • SortOrder — (String)

    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.

    Possible values include:
    • "asc"
    • "dsc"
  • NextToken — (String)

    An ID of the page in the paginated results.

  • Limit — (Integer)

    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.

Callback (callback):

• function(err, data) { ... }

  Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.

  Context (this):

  • (AWS.Response) —

    the response object containing error, data properties, and the original request object.

  Parameters:

  • err (Error) —

    the error object returned from the request. Set to null if the request is successful.

  • data (Object) —

    the de-serialized data returned from the request. Set to null if a request error occurs. The data object has the following properties:

    • Results — (Array<map>)

      A list of BatchPrediction objects that meet the search criteria.

      • BatchPredictionId — (String)

        The ID assigned to the BatchPrediction at creation. This value should be identical to the value of the BatchPredictionID in the request.

      • MLModelId — (String)

        The ID of the MLModel that generated predictions for the BatchPrediction request.

      • BatchPredictionDataSourceId — (String)

        The ID of the DataSource that points to the group of observations to predict.

      • InputDataLocationS3 — (String)

        The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

      • CreatedByIamUser — (String)

        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.

      • CreatedAt — (Date)

        The time that the BatchPrediction was created. The time is expressed in epoch time.

      • LastUpdatedAt — (Date)

        The time of the most recent edit to the BatchPrediction. The time is expressed in epoch time.

      • Name — (String)

        A user-supplied name or description of the BatchPrediction.

      • Status — (String)

        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.

        Possible values include:
        • "PENDING"
        • "INPROGRESS"
        • "FAILED"
        • "COMPLETED"
        • "DELETED"
      • OutputUri — (String)

        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: ':', '//', '/./', '/../'.

      • Message — (String)

        A description of the most recent details about processing the batch prediction request.

      • ComputeTime — (Integer)

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

      • FinishedAt — (Date)

        A timestamp represented in epoch time.

      • StartedAt — (Date)

        A timestamp represented in epoch time.

      • TotalRecordCount — (Integer)

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

      • InvalidRecordCount — (Integer)

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

    • NextToken — (String)

      The ID of the next page in the paginated results that indicates at least one more page follows.

Returns:

• (AWS.Request) —

  a handle to the operation request for subsequent event callback registration.

Waiter Resource States:

• batchPredictionAvailable

var params = {
  EQ: 'STRING_VALUE',
  FilterVariable: CreatedAt | LastUpdatedAt | Status | Name | DataLocationS3 | IAMUser,
  GE: 'STRING_VALUE',
  GT: 'STRING_VALUE',
  LE: 'STRING_VALUE',
  LT: 'STRING_VALUE',
  Limit: 'NUMBER_VALUE',
  NE: 'STRING_VALUE',
  NextToken: 'STRING_VALUE',
  Prefix: 'STRING_VALUE',
  SortOrder: asc | dsc
};
machinelearning.describeDataSources(params, function(err, data) {
  if (err) console.log(err, err.stack); // an error occurred
  else     console.log(data);           // successful response
});

Parameters:

• params (Object) (defaults to: {}) —
  • FilterVariable — (String)

    Use one of the following variables to filter a list of DataSource:

    • CreatedAt - Sets the search criteria to DataSource creation dates.

    • 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.

    Possible values include:
    • "CreatedAt"
    • "LastUpdatedAt"
    • "Status"
    • "Name"
    • "DataLocationS3"
    • "IAMUser"
  • EQ — (String)

    The equal to operator. The DataSource results will have FilterVariable values that exactly match the value specified with EQ.

  • GT — (String)

    The greater than operator. The DataSource results will have FilterVariable values that are greater than the value specified with GT.

  • LT — (String)

    The less than operator. The DataSource results will have FilterVariable values that are less than the value specified with LT.

  • GE — (String)

    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.

  • LE — (String)

    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.

  • NE — (String)

    The not equal to operator. The DataSource results will have FilterVariable values not equal to the value specified with NE.

  • Prefix — (String)

    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

  • SortOrder — (String)

    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.

    Possible values include:
    • "asc"
    • "dsc"
  • NextToken — (String)

    The ID of the page in the paginated results.

  • Limit — (Integer)

    The maximum number of DataSource to include in the result.

Callback (callback):

• function(err, data) { ... }

  Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.

  Context (this):

  • (AWS.Response) —

    the response object containing error, data properties, and the original request object.

  Parameters:

  • err (Error) —

    the error object returned from the request. Set to null if the request is successful.

  • data (Object) —

    the de-serialized data returned from the request. Set to null if a request error occurs. The data object has the following properties:

    • Results — (Array<map>)

      A list of DataSource that meet the search criteria.

      • DataSourceId — (String)

        The ID that is assigned to the DataSource during creation.

      • DataLocationS3 — (String)

        The location and name of the data in Amazon Simple Storage Service (Amazon S3) that is used by a DataSource.

      • DataRearrangement — (String)

        A JSON string that represents the splitting and rearrangement requirement used when this DataSource was created.

      • CreatedByIamUser — (String)

        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.

      • CreatedAt — (Date)

        The time that the DataSource was created. The time is expressed in epoch time.

      • LastUpdatedAt — (Date)

        The time of the most recent edit to the BatchPrediction. The time is expressed in epoch time.

      • DataSizeInBytes — (Integer)

        The total number of observations contained in the data files that the DataSource references.

      • NumberOfFiles — (Integer)

        The number of data files referenced by the DataSource.

      • Name — (String)

        A user-supplied name or description of the DataSource.

      • Status — (String)

        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.

        Possible values include:
        • "PENDING"
        • "INPROGRESS"
        • "FAILED"
        • "COMPLETED"
        • "DELETED"
      • Message — (String)

        A description of the most recent details about creating the DataSource.

      • RedshiftMetadata — (map)

        Describes the DataSource details specific to Amazon Redshift.

        • RedshiftDatabase — (map)

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

          • DatabaseName — required — (String)

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

          • ClusterIdentifier — required — (String)

            The ID of an Amazon Redshift cluster.

        • DatabaseUserName — (String)

          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.

        • SelectSqlQuery — (String)

          The SQL query that is specified during CreateDataSourceFromRedshift. Returns only if Verbose is true in GetDataSourceInput.

      • RDSMetadata — (map)

        The datasource details that are specific to Amazon RDS.

        • Database — (map)

          The database details required to connect to an Amazon RDS.

          • InstanceIdentifier — required — (String)

            The ID of an RDS DB instance.

          • DatabaseName — required — (String)

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

        • DatabaseUserName — (String)

          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.

        • SelectSqlQuery — (String)

          The SQL query that is supplied during CreateDataSourceFromRDS. Returns only if Verbose is true in GetDataSourceInput.

        • ResourceRole — (String)

          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.

        • ServiceRole — (String)

          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.

        • DataPipelineId — (String)

          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.

      • RoleARN — (String)

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

      • ComputeStatistics — (Boolean)

        The parameter is true if statistics need to be generated from the observation data.

      • ComputeTime — (Integer)

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

      • FinishedAt — (Date)

        A timestamp represented in epoch time.

      • StartedAt — (Date)

        A timestamp represented in epoch time.

    • NextToken — (String)

      An ID of the next page in the paginated results that indicates at least one more page follows.

Returns:

• (AWS.Request) —

  a handle to the operation request for subsequent event callback registration.

Waiter Resource States:

• dataSourceAvailable

var params = {
  EQ: 'STRING_VALUE',
  FilterVariable: CreatedAt | LastUpdatedAt | Status | Name | IAMUser | MLModelId | DataSourceId | DataURI,
  GE: 'STRING_VALUE',
  GT: 'STRING_VALUE',
  LE: 'STRING_VALUE',
  LT: 'STRING_VALUE',
  Limit: 'NUMBER_VALUE',
  NE: 'STRING_VALUE',
  NextToken: 'STRING_VALUE',
  Prefix: 'STRING_VALUE',
  SortOrder: asc | dsc
};
machinelearning.describeEvaluations(params, function(err, data) {
  if (err) console.log(err, err.stack); // an error occurred
  else     console.log(data);           // successful response
});

Parameters:

• params (Object) (defaults to: {}) —
  • FilterVariable — (String)

    Use one of the following variable to filter a list of Evaluation objects:

    • 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.

    Possible values include:
    • "CreatedAt"
    • "LastUpdatedAt"
    • "Status"
    • "Name"
    • "IAMUser"
    • "MLModelId"
    • "DataSourceId"
    • "DataURI"
  • EQ — (String)

    The equal to operator. The Evaluation results will have FilterVariable values that exactly match the value specified with EQ.

  • GT — (String)

    The greater than operator. The Evaluation results will have FilterVariable values that are greater than the value specified with GT.

  • LT — (String)

    The less than operator. The Evaluation results will have FilterVariable values that are less than the value specified with LT.

  • GE — (String)

    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.

  • LE — (String)

    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.

  • NE — (String)

    The not equal to operator. The Evaluation results will have FilterVariable values not equal to the value specified with NE.

  • Prefix — (String)

    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

  • SortOrder — (String)

    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.

    Possible values include:
    • "asc"
    • "dsc"
  • NextToken — (String)

    The ID of the page in the paginated results.

  • Limit — (Integer)

    The maximum number of Evaluation to include in the result.

Callback (callback):

• function(err, data) { ... }

  Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.

  Context (this):

  • (AWS.Response) —

    the response object containing error, data properties, and the original request object.

  Parameters:

  • err (Error) —

    the error object returned from the request. Set to null if the request is successful.

  • data (Object) —

    the de-serialized data returned from the request. Set to null if a request error occurs. The data object has the following properties:

    • Results — (Array<map>)

      A list of Evaluation that meet the search criteria.

      • EvaluationId — (String)

        The ID that is assigned to the Evaluation at creation.

      • MLModelId — (String)

        The ID of the MLModel that is the focus of the evaluation.

      • EvaluationDataSourceId — (String)

        The ID of the DataSource that is used to evaluate the MLModel.

      • InputDataLocationS3 — (String)

        The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.

      • CreatedByIamUser — (String)

        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.

      • CreatedAt — (Date)

        The time that the Evaluation was created. The time is expressed in epoch time.

      • LastUpdatedAt — (Date)

        The time of the most recent edit to the Evaluation. The time is expressed in epoch time.

      • Name — (String)

        A user-supplied name or description of the Evaluation.

      • Status — (String)

        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.

        Possible values include:
        • "PENDING"
        • "INPROGRESS"
        • "FAILED"
        • "COMPLETED"
        • "DELETED"
      • PerformanceMetrics — (map)

        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.

        • Properties — (map<String>)
      • Message — (String)

        A description of the most recent details about evaluating the MLModel.

      • ComputeTime — (Integer)

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

      • FinishedAt — (Date)

        A timestamp represented in epoch time.

      • StartedAt — (Date)

        A timestamp represented in epoch time.

    • NextToken — (String)

      The ID of the next page in the paginated results that indicates at least one more page follows.

Returns:

• (AWS.Request) —

  a handle to the operation request for subsequent event callback registration.

Waiter Resource States:

• evaluationAvailable

var params = {
  EQ: 'STRING_VALUE',
  FilterVariable: CreatedAt | LastUpdatedAt | Status | Name | IAMUser | TrainingDataSourceId | RealtimeEndpointStatus | MLModelType | Algorithm | TrainingDataURI,
  GE: 'STRING_VALUE',
  GT: 'STRING_VALUE',
  LE: 'STRING_VALUE',
  LT: 'STRING_VALUE',
  Limit: 'NUMBER_VALUE',
  NE: 'STRING_VALUE',
  NextToken: 'STRING_VALUE',
  Prefix: 'STRING_VALUE',
  SortOrder: asc | dsc
};
machinelearning.describeMLModels(params, function(err, data) {
  if (err) console.log(err, err.stack); // an error occurred
  else     console.log(data);           // successful response
});

Parameters:

• params (Object) (defaults to: {}) —
  • FilterVariable — (String)

    Use one of the following variables to filter a list of MLModel:

    • 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.

    Possible values include:
    • "CreatedAt"
    • "LastUpdatedAt"
    • "Status"
    • "Name"
    • "IAMUser"
    • "TrainingDataSourceId"
    • "RealtimeEndpointStatus"
    • "MLModelType"
    • "Algorithm"
    • "TrainingDataURI"
  • EQ — (String)

    The equal to operator. The MLModel results will have FilterVariable values that exactly match the value specified with EQ.

  • GT — (String)

    The greater than operator. The MLModel results will have FilterVariable values that are greater than the value specified with GT.

  • LT — (String)

    The less than operator. The MLModel results will have FilterVariable values that are less than the value specified with LT.

  • GE — (String)

    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.

  • LE — (String)

    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.

  • NE — (String)

    The not equal to operator. The MLModel results will have FilterVariable values not equal to the value specified with NE.

  • Prefix — (String)

    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

  • SortOrder — (String)

    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).

    • dsc - Arranges the list in descending order (Z-A, 9-0).

    Results are sorted by FilterVariable.

    Possible values include:
    • "asc"
    • "dsc"
  • NextToken — (String)

    The ID of the page in the paginated results.

  • Limit — (Integer)

    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.

Callback (callback):

• function(err, data) { ... }

  Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.

  Context (this):

  • (AWS.Response) —

    the response object containing error, data properties, and the original request object.

  Parameters:

  • err (Error) —

    the error object returned from the request. Set to null if the request is successful.

  • data (Object) —

    the de-serialized data returned from the request. Set to null if a request error occurs. The data object has the following properties:

    • Results — (Array<map>)

      A list of MLModel that meet the search criteria.

      • MLModelId — (String)

        The ID assigned to the MLModel at creation.

      • TrainingDataSourceId — (String)

        The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.

      • CreatedByIamUser — (String)

        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.

      • CreatedAt — (Date)

        The time that the MLModel was created. The time is expressed in epoch time.

      • LastUpdatedAt — (Date)

        The time of the most recent edit to the MLModel. The time is expressed in epoch time.

      • Name — (String)

        A user-supplied name or description of the MLModel.

      • Status — (String)

        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.

        Possible values include:
        • "PENDING"
        • "INPROGRESS"
        • "FAILED"
        • "COMPLETED"
        • "DELETED"
      • SizeInBytes — (Integer)

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

      • EndpointInfo — (map)

        The current endpoint of the MLModel.

        • PeakRequestsPerSecond — (Integer)

          The maximum processing rate for the real-time endpoint for MLModel, measured in incoming requests per second.

        • CreatedAt — (Date)

          The time that the request to create the real-time endpoint for the MLModel was received. The time is expressed in epoch time.

        • EndpointUrl — (String)

          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.

        • EndpointStatus — (String)

          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.

          Possible values include:
          • "NONE"
          • "READY"
          • "UPDATING"
          • "FAILED"
      • TrainingParameters — (map<String>)

        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 10000. 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.

        • 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.

      • InputDataLocationS3 — (String)

        The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

      • Algorithm — (String)

        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.

        Possible values include:
        • "sgd"
      • MLModelType — (String)

        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?".

        Possible values include:
        • "REGRESSION"
        • "BINARY"
        • "MULTICLASS"
      • ScoreThreshold — (Float)
      • ScoreThresholdLastUpdatedAt — (Date)

        The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.

      • Message — (String)

        A description of the most recent details about accessing the MLModel.

      • ComputeTime — (Integer)

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

      • FinishedAt — (Date)

        A timestamp represented in epoch time.

      • StartedAt — (Date)

        A timestamp represented in epoch time.

    • NextToken — (String)

      The ID of the next page in the paginated results that indicates at least one more page follows.

Returns:

• (AWS.Request) —

  a handle to the operation request for subsequent event callback registration.

Waiter Resource States:

• mLModelAvailable

var params = {
  ResourceId: 'STRING_VALUE', /* required */
  ResourceType: BatchPrediction | DataSource | Evaluation | MLModel /* required */
};
machinelearning.describeTags(params, function(err, data) {
  if (err) console.log(err, err.stack); // an error occurred
  else     console.log(data);           // successful response
});

Parameters:

• params (Object) (defaults to: {}) —
  • ResourceId — (String)

    The ID of the ML object. For example, exampleModelId.

  • ResourceType — (String)

    The type of the ML object.

    Possible values include:
    • "BatchPrediction"
    • "DataSource"
    • "Evaluation"
    • "MLModel"

Callback (callback):

• function(err, data) { ... }

  Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.

  Context (this):

  • (AWS.Response) —

    the response object containing error, data properties, and the original request object.

  Parameters:

  • err (Error) —

    the error object returned from the request. Set to null if the request is successful.

  • data (Object) —

    the de-serialized data returned from the request. Set to null if a request error occurs. The data object has the following properties:

    • ResourceId — (String)

      The ID of the tagged ML object.

    • ResourceType — (String)

      The type of the tagged ML object.

      Possible values include:
      • "BatchPrediction"
      • "DataSource"
      • "Evaluation"
      • "MLModel"
    • Tags — (Array<map>)

      A list of tags associated with the ML object.

      • Key — (String)

        A unique identifier for the tag. Valid characters include Unicode letters, digits, white space, _, ., /, =, +, -, %, and @.

      • Value — (String)

        An optional string, typically used to describe or define the tag. Valid characters include Unicode letters, digits, white space, _, ., /, =, +, -, %, and @.

Returns:

• (AWS.Request) —

  a handle to the operation request for subsequent event callback registration.

var params = {
  BatchPredictionId: 'STRING_VALUE' /* required */
};
machinelearning.getBatchPrediction(params, function(err, data) {
  if (err) console.log(err, err.stack); // an error occurred
  else     console.log(data);           // successful response
});

Parameters:

• params (Object) (defaults to: {}) —
  • BatchPredictionId — (String)

    An ID assigned to the BatchPrediction at creation.

Callback (callback):

• function(err, data) { ... }

  Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.

  Context (this):

  • (AWS.Response) —

    the response object containing error, data properties, and the original request object.

  Parameters:

  • err (Error) —

    the error object returned from the request. Set to null if the request is successful.

  • data (Object) —

    the de-serialized data returned from the request. Set to null if a request error occurs. The data object has the following properties:

    • BatchPredictionId — (String)

      An ID assigned to the BatchPrediction at creation. This value should be identical to the value of the BatchPredictionID in the request.

    • MLModelId — (String)

      The ID of the MLModel that generated predictions for the BatchPrediction request.

    • BatchPredictionDataSourceId — (String)

      The ID of the DataSource that was used to create the BatchPrediction.

    • InputDataLocationS3 — (String)

      The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

    • CreatedByIamUser — (String)

      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.

    • CreatedAt — (Date)

      The time when the BatchPrediction was created. The time is expressed in epoch time.

    • LastUpdatedAt — (Date)

      The time of the most recent edit to BatchPrediction. The time is expressed in epoch time.

    • Name — (String)

      A user-supplied name or description of the BatchPrediction.

    • Status — (String)

      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.

      Possible values include:
      • "PENDING"
      • "INPROGRESS"
      • "FAILED"
      • "COMPLETED"
      • "DELETED"
    • OutputUri — (String)

      The location of an Amazon S3 bucket or directory to receive the operation results.

    • LogUri — (String)

      A link to the file that contains logs of the CreateBatchPrediction operation.

    • Message — (String)

      A description of the most recent details about processing the batch prediction request.

    • ComputeTime — (Integer)

      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.

    • FinishedAt — (Date)

      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.

    • StartedAt — (Date)

      The epoch time when Amazon Machine Learning marked the BatchPrediction as INPROGRESS. StartedAt isn't available if the BatchPrediction is in the PENDING state.

    • TotalRecordCount — (Integer)

      The number of total records that Amazon Machine Learning saw while processing the BatchPrediction.

    • InvalidRecordCount — (Integer)

      The number of invalid records that Amazon Machine Learning saw while processing the BatchPrediction.

Returns:

• (AWS.Request) —

  a handle to the operation request for subsequent event callback registration.

var params = {
  DataSourceId: 'STRING_VALUE', /* required */
  Verbose: true || false
};
machinelearning.getDataSource(params, function(err, data) {
  if (err) console.log(err, err.stack); // an error occurred
  else     console.log(data);           // successful response
});

Parameters:

• params (Object) (defaults to: {}) —
  • DataSourceId — (String)

    The ID assigned to the DataSource at creation.

  • Verbose — (Boolean)

    Specifies whether the GetDataSource operation should return DataSourceSchema.

    If true, DataSourceSchema is returned.

    If false, DataSourceSchema is not returned.

Callback (callback):

• function(err, data) { ... }

  Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.

  Context (this):

  • (AWS.Response) —

    the response object containing error, data properties, and the original request object.

  Parameters:

  • err (Error) —

    the error object returned from the request. Set to null if the request is successful.

  • data (Object) —

    the de-serialized data returned from the request. Set to null if a request error occurs. The data object has the following properties:

    • DataSourceId — (String)

      The ID assigned to the DataSource at creation. This value should be identical to the value of the DataSourceId in the request.

    • DataLocationS3 — (String)

      The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

    • DataRearrangement — (String)

      A JSON string that represents the splitting and rearrangement requirement used when this DataSource was created.

    • CreatedByIamUser — (String)

      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.

    • CreatedAt — (Date)

      The time that the DataSource was created. The time is expressed in epoch time.

    • LastUpdatedAt — (Date)

      The time of the most recent edit to the DataSource. The time is expressed in epoch time.

    • DataSizeInBytes — (Integer)

      The total size of observations in the data files.

    • NumberOfFiles — (Integer)

      The number of data files referenced by the DataSource.

    • Name — (String)

      A user-supplied name or description of the DataSource.

    • Status — (String)

      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.

      Possible values include:
      • "PENDING"
      • "INPROGRESS"
      • "FAILED"
      • "COMPLETED"
      • "DELETED"
    • LogUri — (String)

      A link to the file containing logs of CreateDataSourceFrom* operations.

    • Message — (String)

      The user-supplied description of the most recent details about creating the DataSource.

    • RedshiftMetadata — (map)

      Describes the DataSource details specific to Amazon Redshift.

      • RedshiftDatabase — (map)

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

        • DatabaseName — required — (String)

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

        • ClusterIdentifier — required — (String)

          The ID of an Amazon Redshift cluster.

      • DatabaseUserName — (String)

        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.

      • SelectSqlQuery — (String)

        The SQL query that is specified during CreateDataSourceFromRedshift. Returns only if Verbose is true in GetDataSourceInput.

    • RDSMetadata — (map)

      The datasource details that are specific to Amazon RDS.

      • Database — (map)

        The database details required to connect to an Amazon RDS.

        • InstanceIdentifier — required — (String)

          The ID of an RDS DB instance.

        • DatabaseName — required — (String)

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

      • DatabaseUserName — (String)

        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.

      • SelectSqlQuery — (String)

        The SQL query that is supplied during CreateDataSourceFromRDS. Returns only if Verbose is true in GetDataSourceInput.

      • ResourceRole — (String)

        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.

      • ServiceRole — (String)

        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.

      • DataPipelineId — (String)

        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.

    • RoleARN — (String)

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

    • ComputeStatistics — (Boolean)

      The parameter is true if statistics need to be generated from the observation data.

    • ComputeTime — (Integer)

      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.

    • FinishedAt — (Date)

      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.

    • StartedAt — (Date)

      The epoch time when Amazon Machine Learning marked the DataSource as INPROGRESS. StartedAt isn't available if the DataSource is in the PENDING state.

    • DataSourceSchema — (String)

      The schema used by all of the data files of this DataSource.

      Note: This parameter is provided as part of the verbose format.

Returns:

• (AWS.Request) —

  a handle to the operation request for subsequent event callback registration.

var params = {
  EvaluationId: 'STRING_VALUE' /* required */
};
machinelearning.getEvaluation(params, function(err, data) {
  if (err) console.log(err, err.stack); // an error occurred
  else     console.log(data);           // successful response
});

Parameters:

• params (Object) (defaults to: {}) —
  • EvaluationId — (String)

    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.

Callback (callback):

• function(err, data) { ... }

  Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.

  Context (this):

  • (AWS.Response) —

    the response object containing error, data properties, and the original request object.

  Parameters:

  • err (Error) —

    the error object returned from the request. Set to null if the request is successful.

  • data (Object) —

    the de-serialized data returned from the request. Set to null if a request error occurs. The data object has the following properties:

    • EvaluationId — (String)

      The evaluation ID which is same as the EvaluationId in the request.

    • MLModelId — (String)

      The ID of the MLModel that was the focus of the evaluation.

    • EvaluationDataSourceId — (String)

      The DataSource used for this evaluation.

    • InputDataLocationS3 — (String)

      The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

    • CreatedByIamUser — (String)

      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.

    • CreatedAt — (Date)

      The time that the Evaluation was created. The time is expressed in epoch time.

    • LastUpdatedAt — (Date)

      The time of the most recent edit to the Evaluation. The time is expressed in epoch time.

    • Name — (String)

      A user-supplied name or description of the Evaluation.

    • Status — (String)

      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.

      Possible values include:
      • "PENDING"
      • "INPROGRESS"
      • "FAILED"
      • "COMPLETED"
      • "DELETED"
    • PerformanceMetrics — (map)

      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.

      • Properties — (map<String>)
    • LogUri — (String)

      A link to the file that contains logs of the CreateEvaluation operation.

    • Message — (String)

      A description of the most recent details about evaluating the MLModel.

    • ComputeTime — (Integer)

      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.

    • FinishedAt — (Date)

      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.

    • StartedAt — (Date)

      The epoch time when Amazon Machine Learning marked the Evaluation as INPROGRESS. StartedAt isn't available if the Evaluation is in the PENDING state.

Returns:

• (AWS.Request) —

  a handle to the operation request for subsequent event callback registration.

var params = {
  MLModelId: 'STRING_VALUE', /* required */
  Verbose: true || false
};
machinelearning.getMLModel(params, function(err, data) {
  if (err) console.log(err, err.stack); // an error occurred
  else     console.log(data);           // successful response
});

Parameters:

• params (Object) (defaults to: {}) —
  • MLModelId — (String)

    The ID assigned to the MLModel at creation.

  • Verbose — (Boolean)

    Specifies whether the GetMLModel operation should return Recipe.

    If true, Recipe is returned.

    If false, Recipe is not returned.

Callback (callback):

• function(err, data) { ... }

  Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.

  Context (this):

  • (AWS.Response) —

    the response object containing error, data properties, and the original request object.

  Parameters:

  • err (Error) —

    the error object returned from the request. Set to null if the request is successful.

  • data (Object) —

    the de-serialized data returned from the request. Set to null if a request error occurs. The data object has the following properties:

    • MLModelId — (String)

      The MLModel ID, which is same as the MLModelId in the request.

    • TrainingDataSourceId — (String)

      The ID of the training DataSource.

    • CreatedByIamUser — (String)

      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.

    • CreatedAt — (Date)

      The time that the MLModel was created. The time is expressed in epoch time.

    • LastUpdatedAt — (Date)

      The time of the most recent edit to the MLModel. The time is expressed in epoch time.

    • Name — (String)

      A user-supplied name or description of the MLModel.

    • Status — (String)

      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.

      Possible values include:
      • "PENDING"
      • "INPROGRESS"
      • "FAILED"
      • "COMPLETED"
      • "DELETED"
    • SizeInBytes — (Integer)

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

    • EndpointInfo — (map)

      The current endpoint of the MLModel

      • PeakRequestsPerSecond — (Integer)

        The maximum processing rate for the real-time endpoint for MLModel, measured in incoming requests per second.

      • CreatedAt — (Date)

        The time that the request to create the real-time endpoint for the MLModel was received. The time is expressed in epoch time.

      • EndpointUrl — (String)

        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.

      • EndpointStatus — (String)

        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.

        Possible values include:
        • "NONE"
        • "READY"
        • "UPDATING"
        • "FAILED"
    • TrainingParameters — (map<String>)

      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 10000. 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.

    • InputDataLocationS3 — (String)

      The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

    • MLModelType — (String)

      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?"

      Possible values include:
      • "REGRESSION"
      • "BINARY"
      • "MULTICLASS"
    • ScoreThreshold — (Float)

      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.

    • ScoreThresholdLastUpdatedAt — (Date)

      The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.

    • LogUri — (String)

      A link to the file that contains logs of the CreateMLModel operation.

    • Message — (String)

      A description of the most recent details about accessing the MLModel.

    • ComputeTime — (Integer)

      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.

    • FinishedAt — (Date)

      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.

    • StartedAt — (Date)

      The epoch time when Amazon Machine Learning marked the MLModel as INPROGRESS. StartedAt isn't available if the MLModel is in the PENDING state.

    • Recipe — (String)

      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.

    • Schema — (String)

      The schema used by all of the data files referenced by the DataSource.

      Note: This parameter is provided as part of the verbose format.

Returns:

• (AWS.Request) —

  a handle to the operation request for subsequent event callback registration.

var params = {
  MLModelId: 'STRING_VALUE', /* required */
  PredictEndpoint: 'STRING_VALUE', /* required */
  Record: { /* required */
    '<VariableName>': 'STRING_VALUE',
    /* '<VariableName>': ... */
  }
};
machinelearning.predict(params, function(err, data) {
  if (err) console.log(err, err.stack); // an error occurred
  else     console.log(data);           // successful response
});

Parameters:

• params (Object) (defaults to: {}) —
  • MLModelId — (String)

    A unique identifier of the MLModel.

  • Record — (map<String>)

    A map of variable name-value pairs that represent an observation.

  • PredictEndpoint — (String)

Callback (callback):

• function(err, data) { ... }

  Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.

  Context (this):

  • (AWS.Response) —

    the response object containing error, data properties, and the original request object.

  Parameters:

  • err (Error) —

    the error object returned from the request. Set to null if the request is successful.

  • data (Object) —

    the de-serialized data returned from the request. Set to null if a request error occurs. The data object has the following properties:

    • Prediction — (map)

      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.

      • predictedLabel — (String)

        The prediction label for either a BINARY or MULTICLASS MLModel.

      • predictedValue — (Float)

        The prediction value for REGRESSION MLModel.

      • predictedScores — (map<Float>)

        Provides the raw classification score corresponding to each label.

      • details — (map<String>)

        Provides any additional details regarding the prediction.

Returns:

• (AWS.Request) —

  a handle to the operation request for subsequent event callback registration.

var params = {
  BatchPredictionId: 'STRING_VALUE', /* required */
  BatchPredictionName: 'STRING_VALUE' /* required */
};
machinelearning.updateBatchPrediction(params, function(err, data) {
  if (err) console.log(err, err.stack); // an error occurred
  else     console.log(data);           // successful response
});

Parameters:

• params (Object) (defaults to: {}) —
  • BatchPredictionId — (String)

    The ID assigned to the BatchPrediction during creation.

  • BatchPredictionName — (String)

    A new user-supplied name or description of the BatchPrediction.

Callback (callback):

• function(err, data) { ... }

  Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.

  Context (this):

  • (AWS.Response) —

    the response object containing error, data properties, and the original request object.

  Parameters:

  • err (Error) —

    the error object returned from the request. Set to null if the request is successful.

  • data (Object) —

    the de-serialized data returned from the request. Set to null if a request error occurs. The data object has the following properties:

    • BatchPredictionId — (String)

      The ID assigned to the BatchPrediction during creation. This value should be identical to the value of the BatchPredictionId in the request.

Returns:

• (AWS.Request) —

  a handle to the operation request for subsequent event callback registration.

var params = {
  DataSourceId: 'STRING_VALUE', /* required */
  DataSourceName: 'STRING_VALUE' /* required */
};
machinelearning.updateDataSource(params, function(err, data) {
  if (err) console.log(err, err.stack); // an error occurred
  else     console.log(data);           // successful response
});

Parameters:

• params (Object) (defaults to: {}) —
  • DataSourceId — (String)

    The ID assigned to the DataSource during creation.

  • DataSourceName — (String)

    A new user-supplied name or description of the DataSource that will replace the current description.

Callback (callback):

• function(err, data) { ... }

  Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.

  Context (this):

  • (AWS.Response) —

    the response object containing error, data properties, and the original request object.

  Parameters:

  • err (Error) —

    the error object returned from the request. Set to null if the request is successful.

  • data (Object) —

    the de-serialized data returned from the request. Set to null if a request error occurs. The data object has the following properties:

    • DataSourceId — (String)

      The ID assigned to the DataSource during creation. This value should be identical to the value of the DataSourceID in the request.

Returns:

• (AWS.Request) —

  a handle to the operation request for subsequent event callback registration.

var params = {
  EvaluationId: 'STRING_VALUE', /* required */
  EvaluationName: 'STRING_VALUE' /* required */
};
machinelearning.updateEvaluation(params, function(err, data) {
  if (err) console.log(err, err.stack); // an error occurred
  else     console.log(data);           // successful response
});

Parameters:

• params (Object) (defaults to: {}) —
  • EvaluationId — (String)

    The ID assigned to the Evaluation during creation.

  • EvaluationName — (String)

    A new user-supplied name or description of the Evaluation that will replace the current content.

Callback (callback):

• function(err, data) { ... }

  Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.

  Context (this):

  • (AWS.Response) —

    the response object containing error, data properties, and the original request object.

  Parameters:

  • err (Error) —

    the error object returned from the request. Set to null if the request is successful.

  • data (Object) —

    the de-serialized data returned from the request. Set to null if a request error occurs. The data object has the following properties:

    • EvaluationId — (String)

      The ID assigned to the Evaluation during creation. This value should be identical to the value of the Evaluation in the request.

Returns:

• (AWS.Request) —

  a handle to the operation request for subsequent event callback registration.

var params = {
  MLModelId: 'STRING_VALUE', /* required */
  MLModelName: 'STRING_VALUE',
  ScoreThreshold: 'NUMBER_VALUE'
};
machinelearning.updateMLModel(params, function(err, data) {
  if (err) console.log(err, err.stack); // an error occurred
  else     console.log(data);           // successful response
});

Parameters:

• params (Object) (defaults to: {}) —
  • MLModelId — (String)

    The ID assigned to the MLModel during creation.

  • MLModelName — (String)

    A user-supplied name or description of the MLModel.

  • ScoreThreshold — (Float)

    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.

Callback (callback):

• function(err, data) { ... }

  Called when a response from the service is returned. If a callback is not supplied, you must call AWS.Request.send() on the returned request object to initiate the request.

  Context (this):

  • (AWS.Response) —

    the response object containing error, data properties, and the original request object.

  Parameters:

  • err (Error) —

    the error object returned from the request. Set to null if the request is successful.

  • data (Object) —

    the de-serialized data returned from the request. Set to null if a request error occurs. The data object has the following properties:

    • MLModelId — (String)

      The ID assigned to the MLModel during creation. This value should be identical to the value of the MLModelID in the request.

Returns:

• (AWS.Request) —

  a handle to the operation request for subsequent event callback registration.

var params = {
  // ... input parameters ...
};
machinelearning.waitFor('dataSourceAvailable', params, function(err, data) {
  if (err) console.log(err, err.stack); // an error occurred
  else     console.log(data);           // successful response
});

Parameters:

• state (String) —

  the resource state to wait for. Available states for this service are listed in "Waiter Resource States" below.

• params (map) (defaults to: {}) —

  a list of parameters for the given state. See each waiter resource state for required parameters.

Callback (callback):

• function(err, data) { ... }

  Callback containing error and data information. See the respective resource state for the expected error or data information.

  If the waiter times out its requests, it will return a ResourceNotReady error.

Returns:

• (AWS.Request) —

  a handle to the operation request for subsequent event callback registration.

Waiter Resource States:

• dataSourceAvailable

• mLModelAvailable

• evaluationAvailable

• batchPredictionAvailable