AbstractAmazonMachineLearning (AWS Java SDK for Amazon Machine Learning 1.12.447 API)

java.lang.Object
- com.amazonaws.services.machinelearning.AbstractAmazonMachineLearning

All Implemented Interfaces:

AmazonMachineLearning

Direct Known Subclasses:

AbstractAmazonMachineLearningAsync
```
@Generated(value="com.amazonaws:aws-java-sdk-code-generator")
public class AbstractAmazonMachineLearning
extends Object
implements AmazonMachineLearning
```
Abstract implementation of AmazonMachineLearning. Convenient method forms pass through to the corresponding overload that takes a request object, which throws an UnsupportedOperationException.

Field Summary
- Fields inherited from interface com.amazonaws.services.machinelearning.AmazonMachineLearning
  ENDPOINT_PREFIX

Constructor Summary

Constructors
Modifier Constructor and Description

protected AbstractAmazonMachineLearning()

Constructors
Modifier	Constructor and Description
`protected`	`AbstractAmazonMachineLearning()`

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`AddTagsResult`	`addTags(AddTagsRequest request)` Adds one or more tags to an object, up to a limit of 10.
`CreateBatchPredictionResult`	`createBatchPrediction(CreateBatchPredictionRequest request)` Generates predictions for a group of observations.
`CreateDataSourceFromRDSResult`	`createDataSourceFromRDS(CreateDataSourceFromRDSRequest request)` Creates a `DataSource` object from an Amazon Relational Database Service (Amazon RDS).
`CreateDataSourceFromRedshiftResult`	`createDataSourceFromRedshift(CreateDataSourceFromRedshiftRequest request)` Creates a `DataSource` from a database hosted on an Amazon Redshift cluster.
`CreateDataSourceFromS3Result`	`createDataSourceFromS3(CreateDataSourceFromS3Request request)` Creates a `DataSource` object.
`CreateEvaluationResult`	`createEvaluation(CreateEvaluationRequest request)` Creates a new `Evaluation` of an `MLModel`.
`CreateMLModelResult`	`createMLModel(CreateMLModelRequest request)` Creates a new `MLModel` using the `DataSource` and the recipe as information sources.
`CreateRealtimeEndpointResult`	`createRealtimeEndpoint(CreateRealtimeEndpointRequest request)` Creates a real-time endpoint for the `MLModel`.
`DeleteBatchPredictionResult`	`deleteBatchPrediction(DeleteBatchPredictionRequest request)` Assigns the DELETED status to a `BatchPrediction`, rendering it unusable.
`DeleteDataSourceResult`	`deleteDataSource(DeleteDataSourceRequest request)` Assigns the DELETED status to a `DataSource`, rendering it unusable.
`DeleteEvaluationResult`	`deleteEvaluation(DeleteEvaluationRequest request)` Assigns the `DELETED` status to an `Evaluation`, rendering it unusable.
`DeleteMLModelResult`	`deleteMLModel(DeleteMLModelRequest request)` Assigns the `DELETED` status to an `MLModel`, rendering it unusable.
`DeleteRealtimeEndpointResult`	`deleteRealtimeEndpoint(DeleteRealtimeEndpointRequest request)` Deletes a real time endpoint of an `MLModel`.
`DeleteTagsResult`	`deleteTags(DeleteTagsRequest request)` Deletes the specified tags associated with an ML object.
`DescribeBatchPredictionsResult`	`describeBatchPredictions()` Simplified method form for invoking the DescribeBatchPredictions operation.
`DescribeBatchPredictionsResult`	`describeBatchPredictions(DescribeBatchPredictionsRequest request)` Returns a list of `BatchPrediction` operations that match the search criteria in the request.
`DescribeDataSourcesResult`	`describeDataSources()` Simplified method form for invoking the DescribeDataSources operation.
`DescribeDataSourcesResult`	`describeDataSources(DescribeDataSourcesRequest request)` Returns a list of `DataSource` that match the search criteria in the request.
`DescribeEvaluationsResult`	`describeEvaluations()` Simplified method form for invoking the DescribeEvaluations operation.
`DescribeEvaluationsResult`	`describeEvaluations(DescribeEvaluationsRequest request)` Returns a list of `DescribeEvaluations` that match the search criteria in the request.
`DescribeMLModelsResult`	`describeMLModels()` Simplified method form for invoking the DescribeMLModels operation.
`DescribeMLModelsResult`	`describeMLModels(DescribeMLModelsRequest request)` Returns a list of `MLModel` that match the search criteria in the request.
`DescribeTagsResult`	`describeTags(DescribeTagsRequest request)` Describes one or more of the tags for your Amazon ML object.
`GetBatchPredictionResult`	`getBatchPrediction(GetBatchPredictionRequest request)` Returns a `BatchPrediction` that includes detailed metadata, status, and data file information for a `Batch Prediction` request.
`ResponseMetadata`	`getCachedResponseMetadata(AmazonWebServiceRequest request)` Returns additional metadata for a previously executed successful request, typically used for debugging issues where a service isn't acting as expected.
`GetDataSourceResult`	`getDataSource(GetDataSourceRequest request)` Returns a `DataSource` that includes metadata and data file information, as well as the current status of the `DataSource`.
`GetEvaluationResult`	`getEvaluation(GetEvaluationRequest request)` Returns an `Evaluation` that includes metadata as well as the current status of the `Evaluation`.
`GetMLModelResult`	`getMLModel(GetMLModelRequest request)` Returns an `MLModel` that includes detailed metadata, data source information, and the current status of the `MLModel`.
`PredictResult`	`predict(PredictRequest request)` Generates a prediction for the observation using the specified `ML Model`.
`void`	`setEndpoint(String endpoint)` Overrides the default endpoint for this client ("https://machinelearning.us-east-1.amazonaws.com").
`void`	`setRegion(Region region)` An alternative to `AmazonMachineLearning.setEndpoint(String)`, sets the regional endpoint for this client's service calls.
`void`	`shutdown()` Shuts down this client object, releasing any resources that might be held open.
`UpdateBatchPredictionResult`	`updateBatchPrediction(UpdateBatchPredictionRequest request)` Updates the `BatchPredictionName` of a `BatchPrediction`.
`UpdateDataSourceResult`	`updateDataSource(UpdateDataSourceRequest request)` Updates the `DataSourceName` of a `DataSource`.
`UpdateEvaluationResult`	`updateEvaluation(UpdateEvaluationRequest request)` Updates the `EvaluationName` of an `Evaluation`.
`UpdateMLModelResult`	`updateMLModel(UpdateMLModelRequest request)` Updates the `MLModelName` and the `ScoreThreshold` of an `MLModel`.
`AmazonMachineLearningWaiters`	`waiters()`

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Constructor Detail
  - AbstractAmazonMachineLearning
```
protected AbstractAmazonMachineLearning()
```
- Method Detail
  - setEndpoint
```
public void setEndpoint(String endpoint)
```
    Description copied from interface: AmazonMachineLearning
    
    Overrides the default endpoint for this client ("https://machinelearning.us-east-1.amazonaws.com"). Callers can use this method to control which AWS region they want to work with.
    Callers can pass in just the endpoint (ex: "machinelearning.us-east-1.amazonaws.com") or a full URL, including the protocol (ex: "https://machinelearning.us-east-1.amazonaws.com"). If the protocol is not specified here, the default protocol from this client's ClientConfiguration will be used, which by default is HTTPS.
    For more information on using AWS regions with the AWS SDK for Java, and a complete list of all available endpoints for all AWS services, see: https://docs.aws.amazon.com/sdk-for-java/v1/developer-guide/java-dg-region-selection.html#region-selection- choose-endpoint
    This method is not threadsafe. An endpoint should be configured when the client is created and before any service requests are made. Changing it afterwards creates inevitable race conditions for any service requests in transit or retrying.
    
    Specified by:
    
    setEndpoint in interface AmazonMachineLearning
    
    Parameters:
    
    endpoint - The endpoint (ex: "machinelearning.us-east-1.amazonaws.com") or a full URL, including the protocol (ex: "https://machinelearning.us-east-1.amazonaws.com") of the region specific AWS endpoint this client will communicate with.
  - setRegion
```
public void setRegion(Region region)
```
    Description copied from interface: AmazonMachineLearning
    
    An alternative to AmazonMachineLearning.setEndpoint(String), sets the regional endpoint for this client's service calls. Callers can use this method to control which AWS region they want to work with.
    By default, all service endpoints in all regions use the https protocol. To use http instead, specify it in the ClientConfiguration supplied at construction.
    This method is not threadsafe. A region should be configured when the client is created and before any service requests are made. Changing it afterwards creates inevitable race conditions for any service requests in transit or retrying.
    
    Specified by:
    
    setRegion in interface AmazonMachineLearning
    
    Parameters:
    
    region - The region this client will communicate with. See Region.getRegion(com.amazonaws.regions.Regions) for accessing a given region. Must not be null and must be a region where the service is available.
    
    See Also:
    
    Region.getRegion(com.amazonaws.regions.Regions), Region.createClient(Class, com.amazonaws.auth.AWSCredentialsProvider, ClientConfiguration), Region.isServiceSupported(String)
  - addTags
```
public AddTagsResult addTags(AddTagsRequest request)
```
    Description copied from interface: AmazonMachineLearning
    
    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.
    
    Specified by:
    
    addTags in interface AmazonMachineLearning
    
    Returns:
    
    Result of the AddTags operation returned by the service.
  - createBatchPrediction
```
public CreateBatchPredictionResult createBatchPrediction(CreateBatchPredictionRequest request)
```
    Description copied from interface: AmazonMachineLearning
    
    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.
    
    Specified by:
    
    createBatchPrediction in interface AmazonMachineLearning
    
    Returns:
    
    Result of the CreateBatchPrediction operation returned by the service.
  - createDataSourceFromRDS
```
public CreateDataSourceFromRDSResult createDataSourceFromRDS(CreateDataSourceFromRDSRequest request)
```
    Description copied from interface: AmazonMachineLearning
    
    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.
    
    Specified by:
    
    createDataSourceFromRDS in interface AmazonMachineLearning
    
    Returns:
    
    Result of the CreateDataSourceFromRDS operation returned by the service.
  - createDataSourceFromRedshift
```
public CreateDataSourceFromRedshiftResult createDataSourceFromRedshift(CreateDataSourceFromRedshiftRequest request)
```
    Description copied from interface: AmazonMachineLearning
    
    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.
    
    Specified by:
    
    createDataSourceFromRedshift in interface AmazonMachineLearning
    
    Returns:
    
    Result of the CreateDataSourceFromRedshift operation returned by the service.
  - createDataSourceFromS3
```
public CreateDataSourceFromS3Result createDataSourceFromS3(CreateDataSourceFromS3Request request)
```
    Description copied from interface: AmazonMachineLearning
    
    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.
    
    Specified by:
    
    createDataSourceFromS3 in interface AmazonMachineLearning
    
    Returns:
    
    Result of the CreateDataSourceFromS3 operation returned by the service.
  - createEvaluation
```
public CreateEvaluationResult createEvaluation(CreateEvaluationRequest request)
```
    Description copied from interface: AmazonMachineLearning
    
    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.
    
    Specified by:
    
    createEvaluation in interface AmazonMachineLearning
    
    Returns:
    
    Result of the CreateEvaluation operation returned by the service.
  - createMLModel
```
public CreateMLModelResult createMLModel(CreateMLModelRequest request)
```
    Description copied from interface: AmazonMachineLearning
    
    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.
    
    Specified by:
    
    createMLModel in interface AmazonMachineLearning
    
    Returns:
    
    Result of the CreateMLModel operation returned by the service.
  - createRealtimeEndpoint
```
public CreateRealtimeEndpointResult createRealtimeEndpoint(CreateRealtimeEndpointRequest request)
```
    Description copied from interface: AmazonMachineLearning
    
    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.
    
    Specified by:
    
    createRealtimeEndpoint in interface AmazonMachineLearning
    
    Returns:
    
    Result of the CreateRealtimeEndpoint operation returned by the service.
  - deleteBatchPrediction
```
public DeleteBatchPredictionResult deleteBatchPrediction(DeleteBatchPredictionRequest request)
```
    Description copied from interface: AmazonMachineLearning
    
    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.
    
    Specified by:
    
    deleteBatchPrediction in interface AmazonMachineLearning
    
    Returns:
    
    Result of the DeleteBatchPrediction operation returned by the service.
  - deleteDataSource
```
public DeleteDataSourceResult deleteDataSource(DeleteDataSourceRequest request)
```
    Description copied from interface: AmazonMachineLearning
    
    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.
    
    Specified by:
    
    deleteDataSource in interface AmazonMachineLearning
    
    Returns:
    
    Result of the DeleteDataSource operation returned by the service.
  - deleteEvaluation
```
public DeleteEvaluationResult deleteEvaluation(DeleteEvaluationRequest request)
```
    Description copied from interface: AmazonMachineLearning
    
    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.
    
    Specified by:
    
    deleteEvaluation in interface AmazonMachineLearning
    
    Returns:
    
    Result of the DeleteEvaluation operation returned by the service.
  - deleteMLModel
```
public DeleteMLModelResult deleteMLModel(DeleteMLModelRequest request)
```
    Description copied from interface: AmazonMachineLearning
    
    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.
    
    Specified by:
    
    deleteMLModel in interface AmazonMachineLearning
    
    Returns:
    
    Result of the DeleteMLModel operation returned by the service.
  - deleteRealtimeEndpoint
```
public DeleteRealtimeEndpointResult deleteRealtimeEndpoint(DeleteRealtimeEndpointRequest request)
```
    Description copied from interface: AmazonMachineLearning
    
    Deletes a real time endpoint of an MLModel.
    
    Specified by:
    
    deleteRealtimeEndpoint in interface AmazonMachineLearning
    
    Returns:
    
    Result of the DeleteRealtimeEndpoint operation returned by the service.
  - deleteTags
```
public DeleteTagsResult deleteTags(DeleteTagsRequest request)
```
    Description copied from interface: AmazonMachineLearning
    
    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.
    
    Specified by:
    
    deleteTags in interface AmazonMachineLearning
    
    Returns:
    
    Result of the DeleteTags operation returned by the service.
  - describeBatchPredictions
```
public DescribeBatchPredictionsResult describeBatchPredictions(DescribeBatchPredictionsRequest request)
```
    Description copied from interface: AmazonMachineLearning
    
    Returns a list of BatchPrediction operations that match the search criteria in the request.
    
    Specified by:
    
    describeBatchPredictions in interface AmazonMachineLearning
    
    Returns:
    
    Result of the DescribeBatchPredictions operation returned by the service.
  - describeBatchPredictions
```
public DescribeBatchPredictionsResult describeBatchPredictions()
```
    Description copied from interface: AmazonMachineLearning
    
    Simplified method form for invoking the DescribeBatchPredictions operation.
    
    Specified by:
    
    describeBatchPredictions in interface AmazonMachineLearning
    
    See Also:
    
    AmazonMachineLearning.describeBatchPredictions(DescribeBatchPredictionsRequest)
  - describeDataSources
```
public DescribeDataSourcesResult describeDataSources(DescribeDataSourcesRequest request)
```
    Description copied from interface: AmazonMachineLearning
    
    Returns a list of DataSource that match the search criteria in the request.
    
    Specified by:
    
    describeDataSources in interface AmazonMachineLearning
    
    Returns:
    
    Result of the DescribeDataSources operation returned by the service.
  - describeDataSources
```
public DescribeDataSourcesResult describeDataSources()
```
    Description copied from interface: AmazonMachineLearning
    
    Simplified method form for invoking the DescribeDataSources operation.
    
    Specified by:
    
    describeDataSources in interface AmazonMachineLearning
    
    See Also:
    
    AmazonMachineLearning.describeDataSources(DescribeDataSourcesRequest)
  - describeEvaluations
```
public DescribeEvaluationsResult describeEvaluations(DescribeEvaluationsRequest request)
```
    Description copied from interface: AmazonMachineLearning
    
    Returns a list of DescribeEvaluations that match the search criteria in the request.
    
    Specified by:
    
    describeEvaluations in interface AmazonMachineLearning
    
    Returns:
    
    Result of the DescribeEvaluations operation returned by the service.
  - describeEvaluations
```
public DescribeEvaluationsResult describeEvaluations()
```
    Description copied from interface: AmazonMachineLearning
    
    Simplified method form for invoking the DescribeEvaluations operation.
    
    Specified by:
    
    describeEvaluations in interface AmazonMachineLearning
    
    See Also:
    
    AmazonMachineLearning.describeEvaluations(DescribeEvaluationsRequest)
  - describeMLModels
```
public DescribeMLModelsResult describeMLModels(DescribeMLModelsRequest request)
```
    Description copied from interface: AmazonMachineLearning
    
    Returns a list of MLModel that match the search criteria in the request.
    
    Specified by:
    
    describeMLModels in interface AmazonMachineLearning
    
    Returns:
    
    Result of the DescribeMLModels operation returned by the service.
  - describeMLModels
```
public DescribeMLModelsResult describeMLModels()
```
    Description copied from interface: AmazonMachineLearning
    
    Simplified method form for invoking the DescribeMLModels operation.
    
    Specified by:
    
    describeMLModels in interface AmazonMachineLearning
    
    See Also:
    
    AmazonMachineLearning.describeMLModels(DescribeMLModelsRequest)
  - describeTags
```
public DescribeTagsResult describeTags(DescribeTagsRequest request)
```
    Description copied from interface: AmazonMachineLearning
    
    Describes one or more of the tags for your Amazon ML object.
    
    Specified by:
    
    describeTags in interface AmazonMachineLearning
    
    Returns:
    
    Result of the DescribeTags operation returned by the service.
  - getBatchPrediction
```
public GetBatchPredictionResult getBatchPrediction(GetBatchPredictionRequest request)
```
    Description copied from interface: AmazonMachineLearning
    
    Returns a BatchPrediction that includes detailed metadata, status, and data file information for a Batch Prediction request.
    
    Specified by:
    
    getBatchPrediction in interface AmazonMachineLearning
    
    Returns:
    
    Result of the GetBatchPrediction operation returned by the service.
  - getDataSource
```
public GetDataSourceResult getDataSource(GetDataSourceRequest request)
```
    Description copied from interface: AmazonMachineLearning
    
    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.
    
    Specified by:
    
    getDataSource in interface AmazonMachineLearning
    
    Returns:
    
    Result of the GetDataSource operation returned by the service.
  - getEvaluation
```
public GetEvaluationResult getEvaluation(GetEvaluationRequest request)
```
    Description copied from interface: AmazonMachineLearning
    
    Returns an Evaluation that includes metadata as well as the current status of the Evaluation.
    
    Specified by:
    
    getEvaluation in interface AmazonMachineLearning
    
    Returns:
    
    Result of the GetEvaluation operation returned by the service.
  - getMLModel
```
public GetMLModelResult getMLModel(GetMLModelRequest request)
```
    Description copied from interface: AmazonMachineLearning
    
    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.
    
    Specified by:
    
    getMLModel in interface AmazonMachineLearning
    
    Returns:
    
    Result of the GetMLModel operation returned by the service.
  - predict
```
public PredictResult predict(PredictRequest request)
```
    Description copied from interface: AmazonMachineLearning
    
    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.
    
    Specified by:
    
    predict in interface AmazonMachineLearning
    
    Returns:
    
    Result of the Predict operation returned by the service.
  - updateBatchPrediction
```
public UpdateBatchPredictionResult updateBatchPrediction(UpdateBatchPredictionRequest request)
```
    Description copied from interface: AmazonMachineLearning
    
    Updates the BatchPredictionName of a BatchPrediction.
    
    You can use the GetBatchPrediction operation to view the contents of the updated data element.
    
    Specified by:
    
    updateBatchPrediction in interface AmazonMachineLearning
    
    Returns:
    
    Result of the UpdateBatchPrediction operation returned by the service.
  - updateDataSource
```
public UpdateDataSourceResult updateDataSource(UpdateDataSourceRequest request)
```
    Description copied from interface: AmazonMachineLearning
    
    Updates the DataSourceName of a DataSource.
    
    You can use the GetDataSource operation to view the contents of the updated data element.
    
    Specified by:
    
    updateDataSource in interface AmazonMachineLearning
    
    Returns:
    
    Result of the UpdateDataSource operation returned by the service.
  - updateEvaluation
```
public UpdateEvaluationResult updateEvaluation(UpdateEvaluationRequest request)
```
    Description copied from interface: AmazonMachineLearning
    
    Updates the EvaluationName of an Evaluation.
    
    You can use the GetEvaluation operation to view the contents of the updated data element.
    
    Specified by:
    
    updateEvaluation in interface AmazonMachineLearning
    
    Returns:
    
    Result of the UpdateEvaluation operation returned by the service.
  - updateMLModel
```
public UpdateMLModelResult updateMLModel(UpdateMLModelRequest request)
```
    Description copied from interface: AmazonMachineLearning
    
    Updates the MLModelName and the ScoreThreshold of an MLModel.
    
    You can use the GetMLModel operation to view the contents of the updated data element.
    
    Specified by:
    
    updateMLModel in interface AmazonMachineLearning
    
    Returns:
    
    Result of the UpdateMLModel operation returned by the service.
  - shutdown
```
public void shutdown()
```
    Description copied from interface: AmazonMachineLearning
    
    Shuts down this client object, releasing any resources that might be held open. This is an optional method, and callers are not expected to call it, but can if they want to explicitly release any open resources. Once a client has been shutdown, it should not be used to make any more requests.
    
    Specified by:
    
    shutdown in interface AmazonMachineLearning
  - getCachedResponseMetadata
```
public ResponseMetadata getCachedResponseMetadata(AmazonWebServiceRequest request)
```
    Description copied from interface: AmazonMachineLearning
    
    Returns additional metadata for a previously executed successful request, typically used for debugging issues where a service isn't acting as expected. This data isn't considered part of the result data returned by an operation, so it's available through this separate, diagnostic interface.
    Response metadata is only cached for a limited period of time, so if you need to access this extra diagnostic information for an executed request, you should use this method to retrieve it as soon as possible after executing a request.
    
    Specified by:
    
    getCachedResponseMetadata in interface AmazonMachineLearning
    
    Parameters:
    
    request - The originally executed request.
    
    Returns:
    
    The response metadata for the specified request, or null if none is available.
  - waiters
```
public AmazonMachineLearningWaiters waiters()
```
    Specified by:
    
    waiters in interface AmazonMachineLearning

Class AbstractAmazonMachineLearning

Field Summary

Fields inherited from interface com.amazonaws.services.machinelearning.AmazonMachineLearning

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

AbstractAmazonMachineLearning

Method Detail

setEndpoint

setRegion

addTags

createBatchPrediction

createDataSourceFromRDS

createDataSourceFromRedshift

createDataSourceFromS3

createEvaluation

createMLModel

createRealtimeEndpoint

deleteBatchPrediction

deleteDataSource

deleteEvaluation

deleteMLModel

deleteRealtimeEndpoint

deleteTags

describeBatchPredictions

describeBatchPredictions

describeDataSources

describeDataSources

describeEvaluations

describeEvaluations

describeMLModels

describeMLModels

describeTags

getBatchPrediction

getDataSource

getEvaluation

getMLModel

predict

updateBatchPrediction

updateDataSource

updateEvaluation

updateMLModel

shutdown

getCachedResponseMetadata

waiters