AmazonMachineLearningClient (AWS SDK for Java

java.lang.Object
- com.amazonaws.AmazonWebServiceClient
- - com.amazonaws.services.machinelearning.AmazonMachineLearningClient

All Implemented Interfaces:

AmazonMachineLearning

Direct Known Subclasses:

AmazonMachineLearningAsyncClient
```
@ThreadSafe
 @Generated(value="com.amazonaws:aws-java-sdk-code-generator")
public class AmazonMachineLearningClient
extends AmazonWebServiceClient
implements AmazonMachineLearning
```
Client for accessing Amazon Machine Learning. All service calls made using this client are blocking, and will not return until the service call completes.
Definition of the public APIs exposed by Amazon Machine Learning

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

Constructor Summary

Constructors
Constructor and Description
`AmazonMachineLearningClient()` Deprecated. use `AmazonMachineLearningClientBuilder.defaultClient()`
`AmazonMachineLearningClient(AWSCredentials awsCredentials)` Deprecated. use `AwsClientBuilder.withCredentials(AWSCredentialsProvider)` for example: `AmazonMachineLearningClientBuilder.standard().withCredentials(new AWSStaticCredentialsProvider(awsCredentials)).build();`
`AmazonMachineLearningClient(AWSCredentials awsCredentials, ClientConfiguration clientConfiguration)` Deprecated. use `AwsClientBuilder.withCredentials(AWSCredentialsProvider)` and `AwsClientBuilder.withClientConfiguration(ClientConfiguration)`
`AmazonMachineLearningClient(AWSCredentialsProvider awsCredentialsProvider)` Deprecated. use `AwsClientBuilder.withCredentials(AWSCredentialsProvider)`
`AmazonMachineLearningClient(AWSCredentialsProvider awsCredentialsProvider, ClientConfiguration clientConfiguration)` Deprecated. use `AwsClientBuilder.withCredentials(AWSCredentialsProvider)` and `AwsClientBuilder.withClientConfiguration(ClientConfiguration)`
`AmazonMachineLearningClient(AWSCredentialsProvider awsCredentialsProvider, ClientConfiguration clientConfiguration, RequestMetricCollector requestMetricCollector)` Deprecated. use `AwsClientBuilder.withCredentials(AWSCredentialsProvider)` and `AwsClientBuilder.withClientConfiguration(ClientConfiguration)` and `AwsClientBuilder.withMetricsCollector(RequestMetricCollector)`
`AmazonMachineLearningClient(ClientConfiguration clientConfiguration)` Deprecated. use `AwsClientBuilder.withClientConfiguration(ClientConfiguration)`

Method Summary

All Methods Static 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.
`static AmazonMachineLearningClientBuilder`	`builder()`
`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`	`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
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface com.amazonaws.services.machinelearning.AmazonMachineLearning
setEndpoint, setRegion

- Constructor Detail
  - AmazonMachineLearningClient
```
@Deprecated
public AmazonMachineLearningClient()
```
    Deprecated. use AmazonMachineLearningClientBuilder.defaultClient()
    Constructs a new client to invoke service methods on Amazon Machine Learning. A credentials provider chain will be used that searches for credentials in this order:
    - Environment Variables - AWS_ACCESS_KEY_ID and AWS_SECRET_KEY
    - Java System Properties - aws.accessKeyId and aws.secretKey
    - Instance profile credentials delivered through the Amazon EC2 metadata service
    All service calls made using this new client object are blocking, and will not return until the service call completes.
    See Also:
    
    DefaultAWSCredentialsProviderChain
  - AmazonMachineLearningClient
```
@Deprecated
public AmazonMachineLearningClient(ClientConfiguration clientConfiguration)
```
    Deprecated. use AwsClientBuilder.withClientConfiguration(ClientConfiguration)
    Constructs a new client to invoke service methods on Amazon Machine Learning. A credentials provider chain will be used that searches for credentials in this order:
    - Environment Variables - AWS_ACCESS_KEY_ID and AWS_SECRET_KEY
    - Java System Properties - aws.accessKeyId and aws.secretKey
    - Instance profile credentials delivered through the Amazon EC2 metadata service
    All service calls made using this new client object are blocking, and will not return until the service call completes.
    Parameters:
    
    clientConfiguration - The client configuration options controlling how this client connects to Amazon Machine Learning (ex: proxy settings, retry counts, etc.).
    
    See Also:
    
    DefaultAWSCredentialsProviderChain
  - AmazonMachineLearningClient
```
@Deprecated
public AmazonMachineLearningClient(AWSCredentials awsCredentials)
```
    Deprecated. use AwsClientBuilder.withCredentials(AWSCredentialsProvider) for example: AmazonMachineLearningClientBuilder.standard().withCredentials(new AWSStaticCredentialsProvider(awsCredentials)).build();
    
    Constructs a new client to invoke service methods on Amazon Machine Learning using the specified AWS account credentials.
    All service calls made using this new client object are blocking, and will not return until the service call completes.
    
    Parameters:
    
    awsCredentials - The AWS credentials (access key ID and secret key) to use when authenticating with AWS services.
  - AmazonMachineLearningClient
```
@Deprecated
public AmazonMachineLearningClient(AWSCredentials awsCredentials,
                                               ClientConfiguration clientConfiguration)
```
    Deprecated. use AwsClientBuilder.withCredentials(AWSCredentialsProvider) and AwsClientBuilder.withClientConfiguration(ClientConfiguration)
    
    Constructs a new client to invoke service methods on Amazon Machine Learning using the specified AWS account credentials and client configuration options.
    All service calls made using this new client object are blocking, and will not return until the service call completes.
    
    Parameters:
    
    awsCredentials - The AWS credentials (access key ID and secret key) to use when authenticating with AWS services.
    
    clientConfiguration - The client configuration options controlling how this client connects to Amazon Machine Learning (ex: proxy settings, retry counts, etc.).
  - AmazonMachineLearningClient
```
@Deprecated
public AmazonMachineLearningClient(AWSCredentialsProvider awsCredentialsProvider)
```
    Deprecated. use AwsClientBuilder.withCredentials(AWSCredentialsProvider)
    
    Constructs a new client to invoke service methods on Amazon Machine Learning using the specified AWS account credentials provider.
    All service calls made using this new client object are blocking, and will not return until the service call completes.
    
    Parameters:
    
    awsCredentialsProvider - The AWS credentials provider which will provide credentials to authenticate requests with AWS services.
  - AmazonMachineLearningClient
```
@Deprecated
public AmazonMachineLearningClient(AWSCredentialsProvider awsCredentialsProvider,
                                               ClientConfiguration clientConfiguration)
```
    Deprecated. use AwsClientBuilder.withCredentials(AWSCredentialsProvider) and AwsClientBuilder.withClientConfiguration(ClientConfiguration)
    
    Constructs a new client to invoke service methods on Amazon Machine Learning using the specified AWS account credentials provider and client configuration options.
    All service calls made using this new client object are blocking, and will not return until the service call completes.
    
    Parameters:
    
    awsCredentialsProvider - The AWS credentials provider which will provide credentials to authenticate requests with AWS services.
    
    clientConfiguration - The client configuration options controlling how this client connects to Amazon Machine Learning (ex: proxy settings, retry counts, etc.).
  - AmazonMachineLearningClient
```
@Deprecated
public AmazonMachineLearningClient(AWSCredentialsProvider awsCredentialsProvider,
                                               ClientConfiguration clientConfiguration,
                                               RequestMetricCollector requestMetricCollector)
```
    Deprecated. use AwsClientBuilder.withCredentials(AWSCredentialsProvider) and AwsClientBuilder.withClientConfiguration(ClientConfiguration) and AwsClientBuilder.withMetricsCollector(RequestMetricCollector)
    
    Constructs a new client to invoke service methods on Amazon Machine Learning using the specified AWS account credentials provider, client configuration options, and request metric collector.
    All service calls made using this new client object are blocking, and will not return until the service call completes.
    
    Parameters:
    
    awsCredentialsProvider - The AWS credentials provider which will provide credentials to authenticate requests with AWS services.
    
    clientConfiguration - The client configuration options controlling how this client connects to Amazon Machine Learning (ex: proxy settings, retry counts, etc.).
    
    requestMetricCollector - optional request metric collector
- Method Detail
  - builder
```
public static AmazonMachineLearningClientBuilder builder()
```
  - addTags
```
public AddTagsResult addTags(AddTagsRequest request)
```
    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
    
    Parameters:
    
    addTagsRequest -
    
    Returns:
    
    Result of the AddTags operation returned by the service.
    
    Throws:
    
    InvalidInputException - An error on the client occurred. Typically, the cause is an invalid input value.
    
    InvalidTagException
    
    TagLimitExceededException
    
    ResourceNotFoundException - A specified resource cannot be located.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - createBatchPrediction
```
public CreateBatchPredictionResult createBatchPrediction(CreateBatchPredictionRequest request)
```
    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
    
    Parameters:
    
    createBatchPredictionRequest -
    
    Returns:
    
    Result of the CreateBatchPrediction operation returned by the service.
    
    Throws:
    
    InvalidInputException - An error on the client occurred. Typically, the cause is an invalid input value.
    
    InternalServerException - An error on the server occurred when trying to process a request.
    
    IdempotentParameterMismatchException - A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.
  - createDataSourceFromRDS
```
public CreateDataSourceFromRDSResult createDataSourceFromRDS(CreateDataSourceFromRDSRequest request)
```
    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
    
    Parameters:
    
    createDataSourceFromRDSRequest -
    
    Returns:
    
    Result of the CreateDataSourceFromRDS operation returned by the service.
    
    Throws:
    
    InvalidInputException - An error on the client occurred. Typically, the cause is an invalid input value.
    
    InternalServerException - An error on the server occurred when trying to process a request.
    
    IdempotentParameterMismatchException - A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.
  - createDataSourceFromRedshift
```
public CreateDataSourceFromRedshiftResult createDataSourceFromRedshift(CreateDataSourceFromRedshiftRequest request)
```
    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
    
    Parameters:
    
    createDataSourceFromRedshiftRequest -
    
    Returns:
    
    Result of the CreateDataSourceFromRedshift operation returned by the service.
    
    Throws:
    
    InvalidInputException - An error on the client occurred. Typically, the cause is an invalid input value.
    
    InternalServerException - An error on the server occurred when trying to process a request.
    
    IdempotentParameterMismatchException - A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.
  - createDataSourceFromS3
```
public CreateDataSourceFromS3Result createDataSourceFromS3(CreateDataSourceFromS3Request request)
```
    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
    
    Parameters:
    
    createDataSourceFromS3Request -
    
    Returns:
    
    Result of the CreateDataSourceFromS3 operation returned by the service.
    
    Throws:
    
    InvalidInputException - An error on the client occurred. Typically, the cause is an invalid input value.
    
    InternalServerException - An error on the server occurred when trying to process a request.
    
    IdempotentParameterMismatchException - A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.
  - createEvaluation
```
public CreateEvaluationResult createEvaluation(CreateEvaluationRequest request)
```
    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
    
    Parameters:
    
    createEvaluationRequest -
    
    Returns:
    
    Result of the CreateEvaluation operation returned by the service.
    
    Throws:
    
    InvalidInputException - An error on the client occurred. Typically, the cause is an invalid input value.
    
    InternalServerException - An error on the server occurred when trying to process a request.
    
    IdempotentParameterMismatchException - A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.
  - createMLModel
```
public CreateMLModelResult createMLModel(CreateMLModelRequest request)
```
    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
    
    Parameters:
    
    createMLModelRequest -
    
    Returns:
    
    Result of the CreateMLModel operation returned by the service.
    
    Throws:
    
    InvalidInputException - An error on the client occurred. Typically, the cause is an invalid input value.
    
    InternalServerException - An error on the server occurred when trying to process a request.
    
    IdempotentParameterMismatchException - A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.
  - createRealtimeEndpoint
```
public CreateRealtimeEndpointResult createRealtimeEndpoint(CreateRealtimeEndpointRequest request)
```
    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
    
    Parameters:
    
    createRealtimeEndpointRequest -
    
    Returns:
    
    Result of the CreateRealtimeEndpoint operation returned by the service.
    
    Throws:
    
    InvalidInputException - An error on the client occurred. Typically, the cause is an invalid input value.
    
    ResourceNotFoundException - A specified resource cannot be located.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - deleteBatchPrediction
```
public DeleteBatchPredictionResult deleteBatchPrediction(DeleteBatchPredictionRequest request)
```
    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
    
    Parameters:
    
    deleteBatchPredictionRequest -
    
    Returns:
    
    Result of the DeleteBatchPrediction operation returned by the service.
    
    Throws:
    
    InvalidInputException - An error on the client occurred. Typically, the cause is an invalid input value.
    
    ResourceNotFoundException - A specified resource cannot be located.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - deleteDataSource
```
public DeleteDataSourceResult deleteDataSource(DeleteDataSourceRequest request)
```
    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
    
    Parameters:
    
    deleteDataSourceRequest -
    
    Returns:
    
    Result of the DeleteDataSource operation returned by the service.
    
    Throws:
    
    InvalidInputException - An error on the client occurred. Typically, the cause is an invalid input value.
    
    ResourceNotFoundException - A specified resource cannot be located.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - deleteEvaluation
```
public DeleteEvaluationResult deleteEvaluation(DeleteEvaluationRequest request)
```
    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
    
    Parameters:
    
    deleteEvaluationRequest -
    
    Returns:
    
    Result of the DeleteEvaluation operation returned by the service.
    
    Throws:
    
    InvalidInputException - An error on the client occurred. Typically, the cause is an invalid input value.
    
    ResourceNotFoundException - A specified resource cannot be located.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - deleteMLModel
```
public DeleteMLModelResult deleteMLModel(DeleteMLModelRequest request)
```
    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
    
    Parameters:
    
    deleteMLModelRequest -
    
    Returns:
    
    Result of the DeleteMLModel operation returned by the service.
    
    Throws:
    
    InvalidInputException - An error on the client occurred. Typically, the cause is an invalid input value.
    
    ResourceNotFoundException - A specified resource cannot be located.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - deleteRealtimeEndpoint
```
public DeleteRealtimeEndpointResult deleteRealtimeEndpoint(DeleteRealtimeEndpointRequest request)
```
    Deletes a real time endpoint of an MLModel.
    
    Specified by:
    
    deleteRealtimeEndpoint in interface AmazonMachineLearning
    
    Parameters:
    
    deleteRealtimeEndpointRequest -
    
    Returns:
    
    Result of the DeleteRealtimeEndpoint operation returned by the service.
    
    Throws:
    
    InvalidInputException - An error on the client occurred. Typically, the cause is an invalid input value.
    
    ResourceNotFoundException - A specified resource cannot be located.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - deleteTags
```
public DeleteTagsResult deleteTags(DeleteTagsRequest request)
```
    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
    
    Parameters:
    
    deleteTagsRequest -
    
    Returns:
    
    Result of the DeleteTags operation returned by the service.
    
    Throws:
    
    InvalidInputException - An error on the client occurred. Typically, the cause is an invalid input value.
    
    InvalidTagException
    
    ResourceNotFoundException - A specified resource cannot be located.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - describeBatchPredictions
```
public DescribeBatchPredictionsResult describeBatchPredictions(DescribeBatchPredictionsRequest request)
```
    Returns a list of BatchPrediction operations that match the search criteria in the request.
    
    Specified by:
    
    describeBatchPredictions in interface AmazonMachineLearning
    
    Parameters:
    
    describeBatchPredictionsRequest -
    
    Returns:
    
    Result of the DescribeBatchPredictions operation returned by the service.
    
    Throws:
    
    InvalidInputException - An error on the client occurred. Typically, the cause is an invalid input value.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - 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)
```
    Returns a list of DataSource that match the search criteria in the request.
    
    Specified by:
    
    describeDataSources in interface AmazonMachineLearning
    
    Parameters:
    
    describeDataSourcesRequest -
    
    Returns:
    
    Result of the DescribeDataSources operation returned by the service.
    
    Throws:
    
    InvalidInputException - An error on the client occurred. Typically, the cause is an invalid input value.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - 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)
```
    Returns a list of DescribeEvaluations that match the search criteria in the request.
    
    Specified by:
    
    describeEvaluations in interface AmazonMachineLearning
    
    Parameters:
    
    describeEvaluationsRequest -
    
    Returns:
    
    Result of the DescribeEvaluations operation returned by the service.
    
    Throws:
    
    InvalidInputException - An error on the client occurred. Typically, the cause is an invalid input value.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - 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)
```
    Returns a list of MLModel that match the search criteria in the request.
    
    Specified by:
    
    describeMLModels in interface AmazonMachineLearning
    
    Parameters:
    
    describeMLModelsRequest -
    
    Returns:
    
    Result of the DescribeMLModels operation returned by the service.
    
    Throws:
    
    InvalidInputException - An error on the client occurred. Typically, the cause is an invalid input value.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - 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)
```
    Describes one or more of the tags for your Amazon ML object.
    
    Specified by:
    
    describeTags in interface AmazonMachineLearning
    
    Parameters:
    
    describeTagsRequest -
    
    Returns:
    
    Result of the DescribeTags operation returned by the service.
    
    Throws:
    
    InvalidInputException - An error on the client occurred. Typically, the cause is an invalid input value.
    
    ResourceNotFoundException - A specified resource cannot be located.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - getBatchPrediction
```
public GetBatchPredictionResult getBatchPrediction(GetBatchPredictionRequest request)
```
    Returns a BatchPrediction that includes detailed metadata, status, and data file information for a Batch Prediction request.
    
    Specified by:
    
    getBatchPrediction in interface AmazonMachineLearning
    
    Parameters:
    
    getBatchPredictionRequest -
    
    Returns:
    
    Result of the GetBatchPrediction operation returned by the service.
    
    Throws:
    
    InvalidInputException - An error on the client occurred. Typically, the cause is an invalid input value.
    
    ResourceNotFoundException - A specified resource cannot be located.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - getDataSource
```
public GetDataSourceResult getDataSource(GetDataSourceRequest request)
```
    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
    
    Parameters:
    
    getDataSourceRequest -
    
    Returns:
    
    Result of the GetDataSource operation returned by the service.
    
    Throws:
    
    InvalidInputException - An error on the client occurred. Typically, the cause is an invalid input value.
    
    ResourceNotFoundException - A specified resource cannot be located.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - getEvaluation
```
public GetEvaluationResult getEvaluation(GetEvaluationRequest request)
```
    Returns an Evaluation that includes metadata as well as the current status of the Evaluation.
    
    Specified by:
    
    getEvaluation in interface AmazonMachineLearning
    
    Parameters:
    
    getEvaluationRequest -
    
    Returns:
    
    Result of the GetEvaluation operation returned by the service.
    
    Throws:
    
    InvalidInputException - An error on the client occurred. Typically, the cause is an invalid input value.
    
    ResourceNotFoundException - A specified resource cannot be located.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - getMLModel
```
public GetMLModelResult getMLModel(GetMLModelRequest request)
```
    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
    
    Parameters:
    
    getMLModelRequest -
    
    Returns:
    
    Result of the GetMLModel operation returned by the service.
    
    Throws:
    
    InvalidInputException - An error on the client occurred. Typically, the cause is an invalid input value.
    
    ResourceNotFoundException - A specified resource cannot be located.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - predict
```
public PredictResult predict(PredictRequest request)
```
    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
    
    Parameters:
    
    predictRequest -
    
    Returns:
    
    Result of the Predict operation returned by the service.
    
    Throws:
    
    InvalidInputException - An error on the client occurred. Typically, the cause is an invalid input value.
    
    ResourceNotFoundException - A specified resource cannot be located.
    
    LimitExceededException - The subscriber exceeded the maximum number of operations. This exception can occur when listing objects such as DataSource.
    
    InternalServerException - An error on the server occurred when trying to process a request.
    
    PredictorNotMountedException - The exception is thrown when a predict request is made to an unmounted MLModel.
  - updateBatchPrediction
```
public UpdateBatchPredictionResult updateBatchPrediction(UpdateBatchPredictionRequest request)
```
    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
    
    Parameters:
    
    updateBatchPredictionRequest -
    
    Returns:
    
    Result of the UpdateBatchPrediction operation returned by the service.
    
    Throws:
    
    InvalidInputException - An error on the client occurred. Typically, the cause is an invalid input value.
    
    ResourceNotFoundException - A specified resource cannot be located.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - updateDataSource
```
public UpdateDataSourceResult updateDataSource(UpdateDataSourceRequest request)
```
    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
    
    Parameters:
    
    updateDataSourceRequest -
    
    Returns:
    
    Result of the UpdateDataSource operation returned by the service.
    
    Throws:
    
    InvalidInputException - An error on the client occurred. Typically, the cause is an invalid input value.
    
    ResourceNotFoundException - A specified resource cannot be located.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - updateEvaluation
```
public UpdateEvaluationResult updateEvaluation(UpdateEvaluationRequest request)
```
    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
    
    Parameters:
    
    updateEvaluationRequest -
    
    Returns:
    
    Result of the UpdateEvaluation operation returned by the service.
    
    Throws:
    
    InvalidInputException - An error on the client occurred. Typically, the cause is an invalid input value.
    
    ResourceNotFoundException - A specified resource cannot be located.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - updateMLModel
```
public UpdateMLModelResult updateMLModel(UpdateMLModelRequest request)
```
    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
    
    Parameters:
    
    updateMLModelRequest -
    
    Returns:
    
    Result of the UpdateMLModel operation returned by the service.
    
    Throws:
    
    InvalidInputException - An error on the client occurred. Typically, the cause is an invalid input value.
    
    ResourceNotFoundException - A specified resource cannot be located.
    
    InternalServerException - An error on the server occurred when trying to process a request.
  - getCachedResponseMetadata
```
public 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. 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 the 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
  - shutdown
```
public void shutdown()
```
    Description copied from class: AmazonWebServiceClient
    
    Shuts down this client object, releasing any resources that might be held open. If this method is not invoked, resources may be leaked. Once a client has been shutdown, it should not be used to make any more requests.
    
    Specified by:
    
    shutdown in interface AmazonMachineLearning
    
    Overrides:
    
    shutdown in class AmazonWebServiceClient

AWS SDK for Java 1.x API Reference - 1.12.580

Class AmazonMachineLearningClient

Field Summary

Fields inherited from class com.amazonaws.AmazonWebServiceClient

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

Constructor Summary

Method Summary

Methods inherited from class com.amazonaws.AmazonWebServiceClient

Methods inherited from class java.lang.Object

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

Constructor Detail

AmazonMachineLearningClient

AmazonMachineLearningClient

AmazonMachineLearningClient

AmazonMachineLearningClient

AmazonMachineLearningClient

AmazonMachineLearningClient

AmazonMachineLearningClient

Method Detail

builder

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

getCachedResponseMetadata

waiters

shutdown