AmazonMachineLearningClient.java

/*
 * Copyright 2010-2016 Amazon.com, Inc. or its affiliates. All Rights
 * Reserved.
 *
 * Licensed under the Apache License, Version 2.0 (the "License").
 * You may not use this file except in compliance with the License.
 * A copy of the License is located at
 *
 *  http://aws.amazon.com/apache2.0
 *
 * or in the "license" file accompanying this file. This file is distributed
 * on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
 * express or implied. See the License for the specific language governing
 * permissions and limitations under the License.
 */
package com.amazonaws.services.machinelearning;

import org.w3c.dom.*;

import java.net.*;
import java.util.*;
import java.util.Map.Entry;

import org.apache.commons.logging.*;

import com.amazonaws.*;
import com.amazonaws.auth.*;
import com.amazonaws.handlers.*;
import com.amazonaws.http.*;
import com.amazonaws.internal.*;
import com.amazonaws.metrics.*;
import com.amazonaws.regions.*;
import com.amazonaws.transform.*;
import com.amazonaws.util.*;
import com.amazonaws.protocol.json.*;
import com.amazonaws.util.AWSRequestMetrics.Field;
import com.amazonaws.annotation.ThreadSafe;

import com.amazonaws.services.machinelearning.model.*;
import com.amazonaws.services.machinelearning.model.transform.*;

/**
 * Client for accessing Amazon Machine Learning. All service calls made using
 * this client are blocking, and will not return until the service call
 * completes.
 * <p>
 * Definition of the public APIs exposed by Amazon Machine Learning
 */
@ThreadSafe
public class AmazonMachineLearningClient extends AmazonWebServiceClient
        implements AmazonMachineLearning {
    /** Provider for AWS credentials. */
    private AWSCredentialsProvider awsCredentialsProvider;

    private static final Log log = LogFactory
            .getLog(AmazonMachineLearning.class);

    /** Default signing name for the service. */
    private static final String DEFAULT_SIGNING_NAME = "machinelearning";

    /** The region metadata service name for computing region endpoints. */
    private static final String DEFAULT_ENDPOINT_PREFIX = "machinelearning";

    /**
     * Client configuration factory providing ClientConfigurations tailored to
     * this client
     */
    protected static final ClientConfigurationFactory configFactory = new ClientConfigurationFactory();

    private final SdkJsonProtocolFactory protocolFactory = new SdkJsonProtocolFactory(
            new JsonClientMetadata()
                    .withProtocolVersion("1.1")
                    .withSupportsCbor(false)
                    .addErrorMetadata(
                            new JsonErrorShapeMetadata()
                                    .withErrorCode("InternalServerException")
                                    .withModeledClass(
                                            com.amazonaws.services.machinelearning.model.InternalServerException.class))
                    .addErrorMetadata(
                            new JsonErrorShapeMetadata()
                                    .withErrorCode("LimitExceededException")
                                    .withModeledClass(
                                            com.amazonaws.services.machinelearning.model.LimitExceededException.class))
                    .addErrorMetadata(
                            new JsonErrorShapeMetadata()
                                    .withErrorCode("InvalidInputException")
                                    .withModeledClass(
                                            com.amazonaws.services.machinelearning.model.InvalidInputException.class))
                    .addErrorMetadata(
                            new JsonErrorShapeMetadata()
                                    .withErrorCode(
                                            "IdempotentParameterMismatchException")
                                    .withModeledClass(
                                            com.amazonaws.services.machinelearning.model.IdempotentParameterMismatchException.class))
                    .addErrorMetadata(
                            new JsonErrorShapeMetadata()
                                    .withErrorCode(
                                            "PredictorNotMountedException")
                                    .withModeledClass(
                                            com.amazonaws.services.machinelearning.model.PredictorNotMountedException.class))
                    .addErrorMetadata(
                            new JsonErrorShapeMetadata()
                                    .withErrorCode("ResourceNotFoundException")
                                    .withModeledClass(
                                            com.amazonaws.services.machinelearning.model.ResourceNotFoundException.class)));

    /**
     * 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:
     * <ul>
     * <li>Environment Variables - AWS_ACCESS_KEY_ID and AWS_SECRET_KEY</li>
     * <li>Java System Properties - aws.accessKeyId and aws.secretKey</li>
     * <li>Instance profile credentials delivered through the Amazon EC2
     * metadata service</li>
     * </ul>
     *
     * <p>
     * All service calls made using this new client object are blocking, and
     * will not return until the service call completes.
     *
     * @see DefaultAWSCredentialsProviderChain
     */
    public AmazonMachineLearningClient() {
        this(new DefaultAWSCredentialsProviderChain(), configFactory
                .getConfig());
    }

    /**
     * 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:
     * <ul>
     * <li>Environment Variables - AWS_ACCESS_KEY_ID and AWS_SECRET_KEY</li>
     * <li>Java System Properties - aws.accessKeyId and aws.secretKey</li>
     * <li>Instance profile credentials delivered through the Amazon EC2
     * metadata service</li>
     * </ul>
     *
     * <p>
     * All service calls made using this new client object are blocking, and
     * will not return until the service call completes.
     *
     * @param clientConfiguration
     *        The client configuration options controlling how this client
     *        connects to Amazon Machine Learning (ex: proxy settings, retry
     *        counts, etc.).
     *
     * @see DefaultAWSCredentialsProviderChain
     */
    public AmazonMachineLearningClient(ClientConfiguration clientConfiguration) {
        this(new DefaultAWSCredentialsProviderChain(), clientConfiguration);
    }

    /**
     * Constructs a new client to invoke service methods on Amazon Machine
     * Learning using the specified AWS account credentials.
     *
     * <p>
     * All service calls made using this new client object are blocking, and
     * will not return until the service call completes.
     *
     * @param awsCredentials
     *        The AWS credentials (access key ID and secret key) to use when
     *        authenticating with AWS services.
     */
    public AmazonMachineLearningClient(AWSCredentials awsCredentials) {
        this(awsCredentials, configFactory.getConfig());
    }

    /**
     * Constructs a new client to invoke service methods on Amazon Machine
     * Learning using the specified AWS account credentials and client
     * configuration options.
     *
     * <p>
     * All service calls made using this new client object are blocking, and
     * will not return until the service call completes.
     *
     * @param awsCredentials
     *        The AWS credentials (access key ID and secret key) to use when
     *        authenticating with AWS services.
     * @param clientConfiguration
     *        The client configuration options controlling how this client
     *        connects to Amazon Machine Learning (ex: proxy settings, retry
     *        counts, etc.).
     */
    public AmazonMachineLearningClient(AWSCredentials awsCredentials,
            ClientConfiguration clientConfiguration) {
        super(clientConfiguration);
        this.awsCredentialsProvider = new StaticCredentialsProvider(
                awsCredentials);
        init();
    }

    /**
     * Constructs a new client to invoke service methods on Amazon Machine
     * Learning using the specified AWS account credentials provider.
     *
     * <p>
     * All service calls made using this new client object are blocking, and
     * will not return until the service call completes.
     *
     * @param awsCredentialsProvider
     *        The AWS credentials provider which will provide credentials to
     *        authenticate requests with AWS services.
     */
    public AmazonMachineLearningClient(
            AWSCredentialsProvider awsCredentialsProvider) {
        this(awsCredentialsProvider, configFactory.getConfig());
    }

    /**
     * Constructs a new client to invoke service methods on Amazon Machine
     * Learning using the specified AWS account credentials provider and client
     * configuration options.
     *
     * <p>
     * All service calls made using this new client object are blocking, and
     * will not return until the service call completes.
     *
     * @param awsCredentialsProvider
     *        The AWS credentials provider which will provide credentials to
     *        authenticate requests with AWS services.
     * @param clientConfiguration
     *        The client configuration options controlling how this client
     *        connects to Amazon Machine Learning (ex: proxy settings, retry
     *        counts, etc.).
     */
    public AmazonMachineLearningClient(
            AWSCredentialsProvider awsCredentialsProvider,
            ClientConfiguration clientConfiguration) {
        this(awsCredentialsProvider, clientConfiguration, null);
    }

    /**
     * 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.
     *
     * <p>
     * All service calls made using this new client object are blocking, and
     * will not return until the service call completes.
     *
     * @param awsCredentialsProvider
     *        The AWS credentials provider which will provide credentials to
     *        authenticate requests with AWS services.
     * @param clientConfiguration
     *        The client configuration options controlling how this client
     *        connects to Amazon Machine Learning (ex: proxy settings, retry
     *        counts, etc.).
     * @param requestMetricCollector
     *        optional request metric collector
     */
    public AmazonMachineLearningClient(
            AWSCredentialsProvider awsCredentialsProvider,
            ClientConfiguration clientConfiguration,
            RequestMetricCollector requestMetricCollector) {
        super(clientConfiguration, requestMetricCollector);
        this.awsCredentialsProvider = awsCredentialsProvider;
        init();
    }

    private void init() {
        setServiceNameIntern(DEFAULT_SIGNING_NAME);
        setEndpointPrefix(DEFAULT_ENDPOINT_PREFIX);
        // calling this.setEndPoint(...) will also modify the signer accordingly
        setEndpoint("https://machinelearning.us-east-1.amazonaws.com");
        HandlerChainFactory chainFactory = new HandlerChainFactory();
        requestHandler2s
                .addAll(chainFactory
                        .newRequestHandlerChain("/com/amazonaws/services/machinelearning/request.handlers"));
        requestHandler2s
                .addAll(chainFactory
                        .newRequestHandler2Chain("/com/amazonaws/services/machinelearning/request.handler2s"));
    }

    /**
     * <p>
     * Generates predictions for a group of observations. The observations to
     * process exist in one or more data files referenced by a
     * <code>DataSource</code>. This operation creates a new
     * <code>BatchPrediction</code>, and uses an <code>MLModel</code> and the
     * data files referenced by the <code>DataSource</code> as information
     * sources.
     * </p>
     * <p>
     * <code>CreateBatchPrediction</code> is an asynchronous operation. In
     * response to <code>CreateBatchPrediction</code>, Amazon Machine Learning
     * (Amazon ML) immediately returns and sets the <code>BatchPrediction</code>
     * status to <code>PENDING</code>. After the <code>BatchPrediction</code>
     * completes, Amazon ML sets the status to <code>COMPLETED</code>.
     * </p>
     * <p>
     * You can poll for status updates by using the <a>GetBatchPrediction</a>
     * operation and checking the <code>Status</code> parameter of the result.
     * After the <code>COMPLETED</code> status appears, the results are
     * available in the location specified by the <code>OutputUri</code>
     * parameter.
     * </p>
     * 
     * @param createBatchPredictionRequest
     * @return 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.
     * @throws InternalServerException
     *         An error on the server occurred when trying to process a request.
     * @throws 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.
     * @sample AmazonMachineLearning.CreateBatchPrediction
     */
    @Override
    public CreateBatchPredictionResult createBatchPrediction(
            CreateBatchPredictionRequest createBatchPredictionRequest) {
        ExecutionContext executionContext = createExecutionContext(createBatchPredictionRequest);
        AWSRequestMetrics awsRequestMetrics = executionContext
                .getAwsRequestMetrics();
        awsRequestMetrics.startEvent(Field.ClientExecuteTime);
        Request<CreateBatchPredictionRequest> request = null;
        Response<CreateBatchPredictionResult> response = null;

        try {
            awsRequestMetrics.startEvent(Field.RequestMarshallTime);
            try {
                request = new CreateBatchPredictionRequestMarshaller(
                        protocolFactory).marshall(super
                        .beforeMarshalling(createBatchPredictionRequest));
                // Binds the request metrics to the current request.
                request.setAWSRequestMetrics(awsRequestMetrics);
            } finally {
                awsRequestMetrics.endEvent(Field.RequestMarshallTime);
            }

            HttpResponseHandler<AmazonWebServiceResponse<CreateBatchPredictionResult>> responseHandler = protocolFactory
                    .createResponseHandler(new JsonOperationMetadata()
                            .withPayloadJson(true)
                            .withHasStreamingSuccessResponse(false),
                            new CreateBatchPredictionResultJsonUnmarshaller());
            response = invoke(request, responseHandler, executionContext);

            return response.getAwsResponse();

        } finally {

            endClientExecution(awsRequestMetrics, request, response);
        }
    }

    /**
     * <p>
     * Creates a <code>DataSource</code> object from an <a
     * href="http://aws.amazon.com/rds/"> Amazon Relational Database Service</a>
     * (Amazon RDS). A <code>DataSource</code> references data that can be used
     * to perform <a>CreateMLModel</a>, <a>CreateEvaluation</a>, or
     * <a>CreateBatchPrediction</a> operations.
     * </p>
     * <p>
     * <code>CreateDataSourceFromRDS</code> is an asynchronous operation. In
     * response to <code>CreateDataSourceFromRDS</code>, Amazon Machine Learning
     * (Amazon ML) immediately returns and sets the <code>DataSource</code>
     * status to <code>PENDING</code>. After the <code>DataSource</code> is
     * created and ready for use, Amazon ML sets the <code>Status</code>
     * parameter to <code>COMPLETED</code>. <code>DataSource</code> in
     * <code>COMPLETED</code> or <code>PENDING</code> status can only be used to
     * perform <a>CreateMLModel</a>, <a>CreateEvaluation</a>, or
     * <a>CreateBatchPrediction</a> operations.
     * </p>
     * <p>
     * If Amazon ML cannot accept the input source, it sets the
     * <code>Status</code> parameter to <code>FAILED</code> and includes an
     * error message in the <code>Message</code> attribute of the
     * <a>GetDataSource</a> operation response.
     * </p>
     * 
     * @param createDataSourceFromRDSRequest
     * @return 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.
     * @throws InternalServerException
     *         An error on the server occurred when trying to process a request.
     * @throws 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.
     * @sample AmazonMachineLearning.CreateDataSourceFromRDS
     */
    @Override
    public CreateDataSourceFromRDSResult createDataSourceFromRDS(
            CreateDataSourceFromRDSRequest createDataSourceFromRDSRequest) {
        ExecutionContext executionContext = createExecutionContext(createDataSourceFromRDSRequest);
        AWSRequestMetrics awsRequestMetrics = executionContext
                .getAwsRequestMetrics();
        awsRequestMetrics.startEvent(Field.ClientExecuteTime);
        Request<CreateDataSourceFromRDSRequest> request = null;
        Response<CreateDataSourceFromRDSResult> response = null;

        try {
            awsRequestMetrics.startEvent(Field.RequestMarshallTime);
            try {
                request = new CreateDataSourceFromRDSRequestMarshaller(
                        protocolFactory).marshall(super
                        .beforeMarshalling(createDataSourceFromRDSRequest));
                // Binds the request metrics to the current request.
                request.setAWSRequestMetrics(awsRequestMetrics);
            } finally {
                awsRequestMetrics.endEvent(Field.RequestMarshallTime);
            }

            HttpResponseHandler<AmazonWebServiceResponse<CreateDataSourceFromRDSResult>> responseHandler = protocolFactory
                    .createResponseHandler(new JsonOperationMetadata()
                            .withPayloadJson(true)
                            .withHasStreamingSuccessResponse(false),
                            new CreateDataSourceFromRDSResultJsonUnmarshaller());
            response = invoke(request, responseHandler, executionContext);

            return response.getAwsResponse();

        } finally {

            endClientExecution(awsRequestMetrics, request, response);
        }
    }

    /**
     * <p>
     * Creates a <code>DataSource</code> from <a
     * href="http://aws.amazon.com/redshift/">Amazon Redshift</a>. A
     * <code>DataSource</code> references data that can be used to perform
     * either <a>CreateMLModel</a>, <a>CreateEvaluation</a> or
     * <a>CreateBatchPrediction</a> operations.
     * </p>
     * <p>
     * <code>CreateDataSourceFromRedshift</code> is an asynchronous operation.
     * In response to <code>CreateDataSourceFromRedshift</code>, Amazon Machine
     * Learning (Amazon ML) immediately returns and sets the
     * <code>DataSource</code> status to <code>PENDING</code>. After the
     * <code>DataSource</code> is created and ready for use, Amazon ML sets the
     * <code>Status</code> parameter to <code>COMPLETED</code>.
     * <code>DataSource</code> in <code>COMPLETED</code> or <code>PENDING</code>
     * status can only be used to perform <a>CreateMLModel</a>,
     * <a>CreateEvaluation</a>, or <a>CreateBatchPrediction</a> operations.
     * </p>
     * <p>
     * If Amazon ML cannot accept the input source, it sets the
     * <code>Status</code> parameter to <code>FAILED</code> and includes an
     * error message in the <code>Message</code> attribute of the
     * <a>GetDataSource</a> operation response.
     * </p>
     * <p>
     * The observations should exist in the database hosted on an Amazon
     * Redshift cluster and should be specified by a <code>SelectSqlQuery</code>
     * . Amazon ML executes <a href=
     * "http://docs.aws.amazon.com/redshift/latest/dg/t_Unloading_tables.html">
     * Unload</a> command in Amazon Redshift to transfer the result set of
     * <code>SelectSqlQuery</code> to <code>S3StagingLocation.</code>
     * </p>
     * <p>
     * After the <code>DataSource</code> is created, it's ready for use in
     * evaluations and batch predictions. If you plan to use the
     * <code>DataSource</code> to train an <code>MLModel</code>, the
     * <code>DataSource</code> requires another item -- a recipe. A recipe
     * describes the observation variables that participate in training an
     * <code>MLModel</code>. A recipe describes how each input variable will be
     * used in training. Will the variable be included or excluded from
     * training? Will the variable be manipulated, for example, combined with
     * another variable or split apart into word combinations? The recipe
     * provides answers to these questions. For more information, see the Amazon
     * Machine Learning Developer Guide.
     * </p>
     * 
     * @param createDataSourceFromRedshiftRequest
     * @return 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.
     * @throws InternalServerException
     *         An error on the server occurred when trying to process a request.
     * @throws 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.
     * @sample AmazonMachineLearning.CreateDataSourceFromRedshift
     */
    @Override
    public CreateDataSourceFromRedshiftResult createDataSourceFromRedshift(
            CreateDataSourceFromRedshiftRequest createDataSourceFromRedshiftRequest) {
        ExecutionContext executionContext = createExecutionContext(createDataSourceFromRedshiftRequest);
        AWSRequestMetrics awsRequestMetrics = executionContext
                .getAwsRequestMetrics();
        awsRequestMetrics.startEvent(Field.ClientExecuteTime);
        Request<CreateDataSourceFromRedshiftRequest> request = null;
        Response<CreateDataSourceFromRedshiftResult> response = null;

        try {
            awsRequestMetrics.startEvent(Field.RequestMarshallTime);
            try {
                request = new CreateDataSourceFromRedshiftRequestMarshaller(
                        protocolFactory)
                        .marshall(super
                                .beforeMarshalling(createDataSourceFromRedshiftRequest));
                // Binds the request metrics to the current request.
                request.setAWSRequestMetrics(awsRequestMetrics);
            } finally {
                awsRequestMetrics.endEvent(Field.RequestMarshallTime);
            }

            HttpResponseHandler<AmazonWebServiceResponse<CreateDataSourceFromRedshiftResult>> responseHandler = protocolFactory
                    .createResponseHandler(
                            new JsonOperationMetadata().withPayloadJson(true)
                                    .withHasStreamingSuccessResponse(false),
                            new CreateDataSourceFromRedshiftResultJsonUnmarshaller());
            response = invoke(request, responseHandler, executionContext);

            return response.getAwsResponse();

        } finally {

            endClientExecution(awsRequestMetrics, request, response);
        }
    }

    /**
     * <p>
     * Creates a <code>DataSource</code> object. A <code>DataSource</code>
     * references data that can be used to perform <a>CreateMLModel</a>,
     * <a>CreateEvaluation</a>, or <a>CreateBatchPrediction</a> operations.
     * </p>
     * <p>
     * <code>CreateDataSourceFromS3</code> is an asynchronous operation. In
     * response to <code>CreateDataSourceFromS3</code>, Amazon Machine Learning
     * (Amazon ML) immediately returns and sets the <code>DataSource</code>
     * status to <code>PENDING</code>. After the <code>DataSource</code> is
     * created and ready for use, Amazon ML sets the <code>Status</code>
     * parameter to <code>COMPLETED</code>. <code>DataSource</code> in
     * <code>COMPLETED</code> or <code>PENDING</code> status can only be used to
     * perform <a>CreateMLModel</a>, <a>CreateEvaluation</a> or
     * <a>CreateBatchPrediction</a> operations.
     * </p>
     * <p>
     * If Amazon ML cannot accept the input source, it sets the
     * <code>Status</code> parameter to <code>FAILED</code> and includes an
     * error message in the <code>Message</code> attribute of the
     * <a>GetDataSource</a> operation response.
     * </p>
     * <p>
     * The observation data used in a <code>DataSource</code> 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)
     * bucket, 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 <code>DataSource</code>.
     * </p>
     * <p>
     * After the <code>DataSource</code> has been created, it's ready to use in
     * evaluations and batch predictions. If you plan to use the
     * <code>DataSource</code> to train an <code>MLModel</code>, the
     * <code>DataSource</code> requires another item: a recipe. A recipe
     * describes the observation variables that participate in training an
     * <code>MLModel</code>. A recipe describes how each input variable will be
     * used in training. Will the variable be included or excluded from
     * training? Will the variable be manipulated, for example, combined with
     * another variable, or split apart into word combinations? The recipe
     * provides answers to these questions. For more information, see the <a
     * href="http://docs.aws.amazon.com/machine-learning/latest/dg">Amazon
     * Machine Learning Developer Guide</a>.
     * </p>
     * 
     * @param createDataSourceFromS3Request
     * @return 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.
     * @throws InternalServerException
     *         An error on the server occurred when trying to process a request.
     * @throws 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.
     * @sample AmazonMachineLearning.CreateDataSourceFromS3
     */
    @Override
    public CreateDataSourceFromS3Result createDataSourceFromS3(
            CreateDataSourceFromS3Request createDataSourceFromS3Request) {
        ExecutionContext executionContext = createExecutionContext(createDataSourceFromS3Request);
        AWSRequestMetrics awsRequestMetrics = executionContext
                .getAwsRequestMetrics();
        awsRequestMetrics.startEvent(Field.ClientExecuteTime);
        Request<CreateDataSourceFromS3Request> request = null;
        Response<CreateDataSourceFromS3Result> response = null;

        try {
            awsRequestMetrics.startEvent(Field.RequestMarshallTime);
            try {
                request = new CreateDataSourceFromS3RequestMarshaller(
                        protocolFactory).marshall(super
                        .beforeMarshalling(createDataSourceFromS3Request));
                // Binds the request metrics to the current request.
                request.setAWSRequestMetrics(awsRequestMetrics);
            } finally {
                awsRequestMetrics.endEvent(Field.RequestMarshallTime);
            }

            HttpResponseHandler<AmazonWebServiceResponse<CreateDataSourceFromS3Result>> responseHandler = protocolFactory
                    .createResponseHandler(new JsonOperationMetadata()
                            .withPayloadJson(true)
                            .withHasStreamingSuccessResponse(false),
                            new CreateDataSourceFromS3ResultJsonUnmarshaller());
            response = invoke(request, responseHandler, executionContext);

            return response.getAwsResponse();

        } finally {

            endClientExecution(awsRequestMetrics, request, response);
        }
    }

    /**
     * <p>
     * Creates a new <code>Evaluation</code> of an <code>MLModel</code>. An
     * <code>MLModel</code> is evaluated on a set of observations associated to
     * a <code>DataSource</code>. Like a <code>DataSource</code> for an
     * <code>MLModel</code>, the <code>DataSource</code> for an
     * <code>Evaluation</code> contains values for the Target Variable. The
     * <code>Evaluation</code> compares the predicted result for each
     * observation to the actual outcome and provides a summary so that you know
     * how effective the <code>MLModel</code> functions on the test data.
     * Evaluation generates a relevant performance metric such as BinaryAUC,
     * RegressionRMSE or MulticlassAvgFScore based on the corresponding
     * <code>MLModelType</code>: <code>BINARY</code>, <code>REGRESSION</code> or
     * <code>MULTICLASS</code>.
     * </p>
     * <p>
     * <code>CreateEvaluation</code> is an asynchronous operation. In response
     * to <code>CreateEvaluation</code>, Amazon Machine Learning (Amazon ML)
     * immediately returns and sets the evaluation status to
     * <code>PENDING</code>. After the <code>Evaluation</code> is created and
     * ready for use, Amazon ML sets the status to <code>COMPLETED</code>.
     * </p>
     * <p>
     * You can use the <a>GetEvaluation</a> operation to check progress of the
     * evaluation during the creation operation.
     * </p>
     * 
     * @param createEvaluationRequest
     * @return 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.
     * @throws InternalServerException
     *         An error on the server occurred when trying to process a request.
     * @throws 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.
     * @sample AmazonMachineLearning.CreateEvaluation
     */
    @Override
    public CreateEvaluationResult createEvaluation(
            CreateEvaluationRequest createEvaluationRequest) {
        ExecutionContext executionContext = createExecutionContext(createEvaluationRequest);
        AWSRequestMetrics awsRequestMetrics = executionContext
                .getAwsRequestMetrics();
        awsRequestMetrics.startEvent(Field.ClientExecuteTime);
        Request<CreateEvaluationRequest> request = null;
        Response<CreateEvaluationResult> response = null;

        try {
            awsRequestMetrics.startEvent(Field.RequestMarshallTime);
            try {
                request = new CreateEvaluationRequestMarshaller(protocolFactory)
                        .marshall(super
                                .beforeMarshalling(createEvaluationRequest));
                // Binds the request metrics to the current request.
                request.setAWSRequestMetrics(awsRequestMetrics);
            } finally {
                awsRequestMetrics.endEvent(Field.RequestMarshallTime);
            }

            HttpResponseHandler<AmazonWebServiceResponse<CreateEvaluationResult>> responseHandler = protocolFactory
                    .createResponseHandler(new JsonOperationMetadata()
                            .withPayloadJson(true)
                            .withHasStreamingSuccessResponse(false),
                            new CreateEvaluationResultJsonUnmarshaller());
            response = invoke(request, responseHandler, executionContext);

            return response.getAwsResponse();

        } finally {

            endClientExecution(awsRequestMetrics, request, response);
        }
    }

    /**
     * <p>
     * Creates a new <code>MLModel</code> using the data files and the recipe as
     * information sources.
     * </p>
     * <p>
     * An <code>MLModel</code> is nearly immutable. Users can only update the
     * <code>MLModelName</code> and the <code>ScoreThreshold</code> in an
     * <code>MLModel</code> without creating a new <code>MLModel</code>.
     * </p>
     * <p>
     * <code>CreateMLModel</code> is an asynchronous operation. In response to
     * <code>CreateMLModel</code>, Amazon Machine Learning (Amazon ML)
     * immediately returns and sets the <code>MLModel</code> status to
     * <code>PENDING</code>. After the <code>MLModel</code> is created and ready
     * for use, Amazon ML sets the status to <code>COMPLETED</code>.
     * </p>
     * <p>
     * You can use the <a>GetMLModel</a> operation to check progress of the
     * <code>MLModel</code> during the creation operation.
     * </p>
     * <p>
     * <a>CreateMLModel</a> requires a <code>DataSource</code> with computed
     * statistics, which can be created by setting
     * <code>ComputeStatistics</code> to <code>true</code> in
     * <a>CreateDataSourceFromRDS</a>, <a>CreateDataSourceFromS3</a>, or
     * <a>CreateDataSourceFromRedshift</a> operations.
     * </p>
     * 
     * @param createMLModelRequest
     * @return 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.
     * @throws InternalServerException
     *         An error on the server occurred when trying to process a request.
     * @throws 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.
     * @sample AmazonMachineLearning.CreateMLModel
     */
    @Override
    public CreateMLModelResult createMLModel(
            CreateMLModelRequest createMLModelRequest) {
        ExecutionContext executionContext = createExecutionContext(createMLModelRequest);
        AWSRequestMetrics awsRequestMetrics = executionContext
                .getAwsRequestMetrics();
        awsRequestMetrics.startEvent(Field.ClientExecuteTime);
        Request<CreateMLModelRequest> request = null;
        Response<CreateMLModelResult> response = null;

        try {
            awsRequestMetrics.startEvent(Field.RequestMarshallTime);
            try {
                request = new CreateMLModelRequestMarshaller(protocolFactory)
                        .marshall(super.beforeMarshalling(createMLModelRequest));
                // Binds the request metrics to the current request.
                request.setAWSRequestMetrics(awsRequestMetrics);
            } finally {
                awsRequestMetrics.endEvent(Field.RequestMarshallTime);
            }

            HttpResponseHandler<AmazonWebServiceResponse<CreateMLModelResult>> responseHandler = protocolFactory
                    .createResponseHandler(new JsonOperationMetadata()
                            .withPayloadJson(true)
                            .withHasStreamingSuccessResponse(false),
                            new CreateMLModelResultJsonUnmarshaller());
            response = invoke(request, responseHandler, executionContext);

            return response.getAwsResponse();

        } finally {

            endClientExecution(awsRequestMetrics, request, response);
        }
    }

    /**
     * <p>
     * Creates a real-time endpoint for the <code>MLModel</code>. The endpoint
     * contains the URI of the <code>MLModel</code>; that is, the location to
     * send real-time prediction requests for the specified <code>MLModel</code>
     * .
     * </p>
     * 
     * @param createRealtimeEndpointRequest
     * @return 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.
     * @throws ResourceNotFoundException
     *         A specified resource cannot be located.
     * @throws InternalServerException
     *         An error on the server occurred when trying to process a request.
     * @sample AmazonMachineLearning.CreateRealtimeEndpoint
     */
    @Override
    public CreateRealtimeEndpointResult createRealtimeEndpoint(
            CreateRealtimeEndpointRequest createRealtimeEndpointRequest) {
        ExecutionContext executionContext = createExecutionContext(createRealtimeEndpointRequest);
        AWSRequestMetrics awsRequestMetrics = executionContext
                .getAwsRequestMetrics();
        awsRequestMetrics.startEvent(Field.ClientExecuteTime);
        Request<CreateRealtimeEndpointRequest> request = null;
        Response<CreateRealtimeEndpointResult> response = null;

        try {
            awsRequestMetrics.startEvent(Field.RequestMarshallTime);
            try {
                request = new CreateRealtimeEndpointRequestMarshaller(
                        protocolFactory).marshall(super
                        .beforeMarshalling(createRealtimeEndpointRequest));
                // Binds the request metrics to the current request.
                request.setAWSRequestMetrics(awsRequestMetrics);
            } finally {
                awsRequestMetrics.endEvent(Field.RequestMarshallTime);
            }

            HttpResponseHandler<AmazonWebServiceResponse<CreateRealtimeEndpointResult>> responseHandler = protocolFactory
                    .createResponseHandler(new JsonOperationMetadata()
                            .withPayloadJson(true)
                            .withHasStreamingSuccessResponse(false),
                            new CreateRealtimeEndpointResultJsonUnmarshaller());
            response = invoke(request, responseHandler, executionContext);

            return response.getAwsResponse();

        } finally {

            endClientExecution(awsRequestMetrics, request, response);
        }
    }

    /**
     * <p>
     * Assigns the DELETED status to a <code>BatchPrediction</code>, rendering
     * it unusable.
     * </p>
     * <p>
     * After using the <code>DeleteBatchPrediction</code> operation, you can use
     * the <a>GetBatchPrediction</a> operation to verify that the status of the
     * <code>BatchPrediction</code> changed to DELETED.
     * </p>
     * <p>
     * <b>Caution:</b> The result of the <code>DeleteBatchPrediction</code>
     * operation is irreversible.
     * </p>
     * 
     * @param deleteBatchPredictionRequest
     * @return 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.
     * @throws ResourceNotFoundException
     *         A specified resource cannot be located.
     * @throws InternalServerException
     *         An error on the server occurred when trying to process a request.
     * @sample AmazonMachineLearning.DeleteBatchPrediction
     */
    @Override
    public DeleteBatchPredictionResult deleteBatchPrediction(
            DeleteBatchPredictionRequest deleteBatchPredictionRequest) {
        ExecutionContext executionContext = createExecutionContext(deleteBatchPredictionRequest);
        AWSRequestMetrics awsRequestMetrics = executionContext
                .getAwsRequestMetrics();
        awsRequestMetrics.startEvent(Field.ClientExecuteTime);
        Request<DeleteBatchPredictionRequest> request = null;
        Response<DeleteBatchPredictionResult> response = null;

        try {
            awsRequestMetrics.startEvent(Field.RequestMarshallTime);
            try {
                request = new DeleteBatchPredictionRequestMarshaller(
                        protocolFactory).marshall(super
                        .beforeMarshalling(deleteBatchPredictionRequest));
                // Binds the request metrics to the current request.
                request.setAWSRequestMetrics(awsRequestMetrics);
            } finally {
                awsRequestMetrics.endEvent(Field.RequestMarshallTime);
            }

            HttpResponseHandler<AmazonWebServiceResponse<DeleteBatchPredictionResult>> responseHandler = protocolFactory
                    .createResponseHandler(new JsonOperationMetadata()
                            .withPayloadJson(true)
                            .withHasStreamingSuccessResponse(false),
                            new DeleteBatchPredictionResultJsonUnmarshaller());
            response = invoke(request, responseHandler, executionContext);

            return response.getAwsResponse();

        } finally {

            endClientExecution(awsRequestMetrics, request, response);
        }
    }

    /**
     * <p>
     * Assigns the DELETED status to a <code>DataSource</code>, rendering it
     * unusable.
     * </p>
     * <p>
     * After using the <code>DeleteDataSource</code> operation, you can use the
     * <a>GetDataSource</a> operation to verify that the status of the
     * <code>DataSource</code> changed to DELETED.
     * </p>
     * <p>
     * <b>Caution:</b> The results of the <code>DeleteDataSource</code>
     * operation are irreversible.
     * </p>
     * 
     * @param deleteDataSourceRequest
     * @return 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.
     * @throws ResourceNotFoundException
     *         A specified resource cannot be located.
     * @throws InternalServerException
     *         An error on the server occurred when trying to process a request.
     * @sample AmazonMachineLearning.DeleteDataSource
     */
    @Override
    public DeleteDataSourceResult deleteDataSource(
            DeleteDataSourceRequest deleteDataSourceRequest) {
        ExecutionContext executionContext = createExecutionContext(deleteDataSourceRequest);
        AWSRequestMetrics awsRequestMetrics = executionContext
                .getAwsRequestMetrics();
        awsRequestMetrics.startEvent(Field.ClientExecuteTime);
        Request<DeleteDataSourceRequest> request = null;
        Response<DeleteDataSourceResult> response = null;

        try {
            awsRequestMetrics.startEvent(Field.RequestMarshallTime);
            try {
                request = new DeleteDataSourceRequestMarshaller(protocolFactory)
                        .marshall(super
                                .beforeMarshalling(deleteDataSourceRequest));
                // Binds the request metrics to the current request.
                request.setAWSRequestMetrics(awsRequestMetrics);
            } finally {
                awsRequestMetrics.endEvent(Field.RequestMarshallTime);
            }

            HttpResponseHandler<AmazonWebServiceResponse<DeleteDataSourceResult>> responseHandler = protocolFactory
                    .createResponseHandler(new JsonOperationMetadata()
                            .withPayloadJson(true)
                            .withHasStreamingSuccessResponse(false),
                            new DeleteDataSourceResultJsonUnmarshaller());
            response = invoke(request, responseHandler, executionContext);

            return response.getAwsResponse();

        } finally {

            endClientExecution(awsRequestMetrics, request, response);
        }
    }

    /**
     * <p>
     * Assigns the <code>DELETED</code> status to an <code>Evaluation</code>,
     * rendering it unusable.
     * </p>
     * <p>
     * After invoking the <code>DeleteEvaluation</code> operation, you can use
     * the <a>GetEvaluation</a> operation to verify that the status of the
     * <code>Evaluation</code> changed to <code>DELETED</code>.
     * </p>
     * <p>
     * <b>Caution:</b> The results of the <code>DeleteEvaluation</code>
     * operation are irreversible.
     * </p>
     * 
     * @param deleteEvaluationRequest
     * @return 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.
     * @throws ResourceNotFoundException
     *         A specified resource cannot be located.
     * @throws InternalServerException
     *         An error on the server occurred when trying to process a request.
     * @sample AmazonMachineLearning.DeleteEvaluation
     */
    @Override
    public DeleteEvaluationResult deleteEvaluation(
            DeleteEvaluationRequest deleteEvaluationRequest) {
        ExecutionContext executionContext = createExecutionContext(deleteEvaluationRequest);
        AWSRequestMetrics awsRequestMetrics = executionContext
                .getAwsRequestMetrics();
        awsRequestMetrics.startEvent(Field.ClientExecuteTime);
        Request<DeleteEvaluationRequest> request = null;
        Response<DeleteEvaluationResult> response = null;

        try {
            awsRequestMetrics.startEvent(Field.RequestMarshallTime);
            try {
                request = new DeleteEvaluationRequestMarshaller(protocolFactory)
                        .marshall(super
                                .beforeMarshalling(deleteEvaluationRequest));
                // Binds the request metrics to the current request.
                request.setAWSRequestMetrics(awsRequestMetrics);
            } finally {
                awsRequestMetrics.endEvent(Field.RequestMarshallTime);
            }

            HttpResponseHandler<AmazonWebServiceResponse<DeleteEvaluationResult>> responseHandler = protocolFactory
                    .createResponseHandler(new JsonOperationMetadata()
                            .withPayloadJson(true)
                            .withHasStreamingSuccessResponse(false),
                            new DeleteEvaluationResultJsonUnmarshaller());
            response = invoke(request, responseHandler, executionContext);

            return response.getAwsResponse();

        } finally {

            endClientExecution(awsRequestMetrics, request, response);
        }
    }

    /**
     * <p>
     * Assigns the DELETED status to an <code>MLModel</code>, rendering it
     * unusable.
     * </p>
     * <p>
     * After using the <code>DeleteMLModel</code> operation, you can use the
     * <a>GetMLModel</a> operation to verify that the status of the
     * <code>MLModel</code> changed to DELETED.
     * </p>
     * <p>
     * <b>Caution:</b> The result of the <code>DeleteMLModel</code> operation is
     * irreversible.
     * </p>
     * 
     * @param deleteMLModelRequest
     * @return 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.
     * @throws ResourceNotFoundException
     *         A specified resource cannot be located.
     * @throws InternalServerException
     *         An error on the server occurred when trying to process a request.
     * @sample AmazonMachineLearning.DeleteMLModel
     */
    @Override
    public DeleteMLModelResult deleteMLModel(
            DeleteMLModelRequest deleteMLModelRequest) {
        ExecutionContext executionContext = createExecutionContext(deleteMLModelRequest);
        AWSRequestMetrics awsRequestMetrics = executionContext
                .getAwsRequestMetrics();
        awsRequestMetrics.startEvent(Field.ClientExecuteTime);
        Request<DeleteMLModelRequest> request = null;
        Response<DeleteMLModelResult> response = null;

        try {
            awsRequestMetrics.startEvent(Field.RequestMarshallTime);
            try {
                request = new DeleteMLModelRequestMarshaller(protocolFactory)
                        .marshall(super.beforeMarshalling(deleteMLModelRequest));
                // Binds the request metrics to the current request.
                request.setAWSRequestMetrics(awsRequestMetrics);
            } finally {
                awsRequestMetrics.endEvent(Field.RequestMarshallTime);
            }

            HttpResponseHandler<AmazonWebServiceResponse<DeleteMLModelResult>> responseHandler = protocolFactory
                    .createResponseHandler(new JsonOperationMetadata()
                            .withPayloadJson(true)
                            .withHasStreamingSuccessResponse(false),
                            new DeleteMLModelResultJsonUnmarshaller());
            response = invoke(request, responseHandler, executionContext);

            return response.getAwsResponse();

        } finally {

            endClientExecution(awsRequestMetrics, request, response);
        }
    }

    /**
     * <p>
     * Deletes a real time endpoint of an <code>MLModel</code>.
     * </p>
     * 
     * @param deleteRealtimeEndpointRequest
     * @return 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.
     * @throws ResourceNotFoundException
     *         A specified resource cannot be located.
     * @throws InternalServerException
     *         An error on the server occurred when trying to process a request.
     * @sample AmazonMachineLearning.DeleteRealtimeEndpoint
     */
    @Override
    public DeleteRealtimeEndpointResult deleteRealtimeEndpoint(
            DeleteRealtimeEndpointRequest deleteRealtimeEndpointRequest) {
        ExecutionContext executionContext = createExecutionContext(deleteRealtimeEndpointRequest);
        AWSRequestMetrics awsRequestMetrics = executionContext
                .getAwsRequestMetrics();
        awsRequestMetrics.startEvent(Field.ClientExecuteTime);
        Request<DeleteRealtimeEndpointRequest> request = null;
        Response<DeleteRealtimeEndpointResult> response = null;

        try {
            awsRequestMetrics.startEvent(Field.RequestMarshallTime);
            try {
                request = new DeleteRealtimeEndpointRequestMarshaller(
                        protocolFactory).marshall(super
                        .beforeMarshalling(deleteRealtimeEndpointRequest));
                // Binds the request metrics to the current request.
                request.setAWSRequestMetrics(awsRequestMetrics);
            } finally {
                awsRequestMetrics.endEvent(Field.RequestMarshallTime);
            }

            HttpResponseHandler<AmazonWebServiceResponse<DeleteRealtimeEndpointResult>> responseHandler = protocolFactory
                    .createResponseHandler(new JsonOperationMetadata()
                            .withPayloadJson(true)
                            .withHasStreamingSuccessResponse(false),
                            new DeleteRealtimeEndpointResultJsonUnmarshaller());
            response = invoke(request, responseHandler, executionContext);

            return response.getAwsResponse();

        } finally {

            endClientExecution(awsRequestMetrics, request, response);
        }
    }

    /**
     * <p>
     * Returns a list of <code>BatchPrediction</code> operations that match the
     * search criteria in the request.
     * </p>
     * 
     * @param describeBatchPredictionsRequest
     * @return 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.
     * @throws InternalServerException
     *         An error on the server occurred when trying to process a request.
     * @sample AmazonMachineLearning.DescribeBatchPredictions
     */
    @Override
    public DescribeBatchPredictionsResult describeBatchPredictions(
            DescribeBatchPredictionsRequest describeBatchPredictionsRequest) {
        ExecutionContext executionContext = createExecutionContext(describeBatchPredictionsRequest);
        AWSRequestMetrics awsRequestMetrics = executionContext
                .getAwsRequestMetrics();
        awsRequestMetrics.startEvent(Field.ClientExecuteTime);
        Request<DescribeBatchPredictionsRequest> request = null;
        Response<DescribeBatchPredictionsResult> response = null;

        try {
            awsRequestMetrics.startEvent(Field.RequestMarshallTime);
            try {
                request = new DescribeBatchPredictionsRequestMarshaller(
                        protocolFactory).marshall(super
                        .beforeMarshalling(describeBatchPredictionsRequest));
                // Binds the request metrics to the current request.
                request.setAWSRequestMetrics(awsRequestMetrics);
            } finally {
                awsRequestMetrics.endEvent(Field.RequestMarshallTime);
            }

            HttpResponseHandler<AmazonWebServiceResponse<DescribeBatchPredictionsResult>> responseHandler = protocolFactory
                    .createResponseHandler(
                            new JsonOperationMetadata().withPayloadJson(true)
                                    .withHasStreamingSuccessResponse(false),
                            new DescribeBatchPredictionsResultJsonUnmarshaller());
            response = invoke(request, responseHandler, executionContext);

            return response.getAwsResponse();

        } finally {

            endClientExecution(awsRequestMetrics, request, response);
        }
    }

    @Override
    public DescribeBatchPredictionsResult describeBatchPredictions() {
        return describeBatchPredictions(new DescribeBatchPredictionsRequest());
    }

    /**
     * <p>
     * Returns a list of <code>DataSource</code> that match the search criteria
     * in the request.
     * </p>
     * 
     * @param describeDataSourcesRequest
     * @return 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.
     * @throws InternalServerException
     *         An error on the server occurred when trying to process a request.
     * @sample AmazonMachineLearning.DescribeDataSources
     */
    @Override
    public DescribeDataSourcesResult describeDataSources(
            DescribeDataSourcesRequest describeDataSourcesRequest) {
        ExecutionContext executionContext = createExecutionContext(describeDataSourcesRequest);
        AWSRequestMetrics awsRequestMetrics = executionContext
                .getAwsRequestMetrics();
        awsRequestMetrics.startEvent(Field.ClientExecuteTime);
        Request<DescribeDataSourcesRequest> request = null;
        Response<DescribeDataSourcesResult> response = null;

        try {
            awsRequestMetrics.startEvent(Field.RequestMarshallTime);
            try {
                request = new DescribeDataSourcesRequestMarshaller(
                        protocolFactory).marshall(super
                        .beforeMarshalling(describeDataSourcesRequest));
                // Binds the request metrics to the current request.
                request.setAWSRequestMetrics(awsRequestMetrics);
            } finally {
                awsRequestMetrics.endEvent(Field.RequestMarshallTime);
            }

            HttpResponseHandler<AmazonWebServiceResponse<DescribeDataSourcesResult>> responseHandler = protocolFactory
                    .createResponseHandler(new JsonOperationMetadata()
                            .withPayloadJson(true)
                            .withHasStreamingSuccessResponse(false),
                            new DescribeDataSourcesResultJsonUnmarshaller());
            response = invoke(request, responseHandler, executionContext);

            return response.getAwsResponse();

        } finally {

            endClientExecution(awsRequestMetrics, request, response);
        }
    }

    @Override
    public DescribeDataSourcesResult describeDataSources() {
        return describeDataSources(new DescribeDataSourcesRequest());
    }

    /**
     * <p>
     * Returns a list of <code>DescribeEvaluations</code> that match the search
     * criteria in the request.
     * </p>
     * 
     * @param describeEvaluationsRequest
     * @return 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.
     * @throws InternalServerException
     *         An error on the server occurred when trying to process a request.
     * @sample AmazonMachineLearning.DescribeEvaluations
     */
    @Override
    public DescribeEvaluationsResult describeEvaluations(
            DescribeEvaluationsRequest describeEvaluationsRequest) {
        ExecutionContext executionContext = createExecutionContext(describeEvaluationsRequest);
        AWSRequestMetrics awsRequestMetrics = executionContext
                .getAwsRequestMetrics();
        awsRequestMetrics.startEvent(Field.ClientExecuteTime);
        Request<DescribeEvaluationsRequest> request = null;
        Response<DescribeEvaluationsResult> response = null;

        try {
            awsRequestMetrics.startEvent(Field.RequestMarshallTime);
            try {
                request = new DescribeEvaluationsRequestMarshaller(
                        protocolFactory).marshall(super
                        .beforeMarshalling(describeEvaluationsRequest));
                // Binds the request metrics to the current request.
                request.setAWSRequestMetrics(awsRequestMetrics);
            } finally {
                awsRequestMetrics.endEvent(Field.RequestMarshallTime);
            }

            HttpResponseHandler<AmazonWebServiceResponse<DescribeEvaluationsResult>> responseHandler = protocolFactory
                    .createResponseHandler(new JsonOperationMetadata()
                            .withPayloadJson(true)
                            .withHasStreamingSuccessResponse(false),
                            new DescribeEvaluationsResultJsonUnmarshaller());
            response = invoke(request, responseHandler, executionContext);

            return response.getAwsResponse();

        } finally {

            endClientExecution(awsRequestMetrics, request, response);
        }
    }

    @Override
    public DescribeEvaluationsResult describeEvaluations() {
        return describeEvaluations(new DescribeEvaluationsRequest());
    }

    /**
     * <p>
     * Returns a list of <code>MLModel</code> that match the search criteria in
     * the request.
     * </p>
     * 
     * @param describeMLModelsRequest
     * @return 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.
     * @throws InternalServerException
     *         An error on the server occurred when trying to process a request.
     * @sample AmazonMachineLearning.DescribeMLModels
     */
    @Override
    public DescribeMLModelsResult describeMLModels(
            DescribeMLModelsRequest describeMLModelsRequest) {
        ExecutionContext executionContext = createExecutionContext(describeMLModelsRequest);
        AWSRequestMetrics awsRequestMetrics = executionContext
                .getAwsRequestMetrics();
        awsRequestMetrics.startEvent(Field.ClientExecuteTime);
        Request<DescribeMLModelsRequest> request = null;
        Response<DescribeMLModelsResult> response = null;

        try {
            awsRequestMetrics.startEvent(Field.RequestMarshallTime);
            try {
                request = new DescribeMLModelsRequestMarshaller(protocolFactory)
                        .marshall(super
                                .beforeMarshalling(describeMLModelsRequest));
                // Binds the request metrics to the current request.
                request.setAWSRequestMetrics(awsRequestMetrics);
            } finally {
                awsRequestMetrics.endEvent(Field.RequestMarshallTime);
            }

            HttpResponseHandler<AmazonWebServiceResponse<DescribeMLModelsResult>> responseHandler = protocolFactory
                    .createResponseHandler(new JsonOperationMetadata()
                            .withPayloadJson(true)
                            .withHasStreamingSuccessResponse(false),
                            new DescribeMLModelsResultJsonUnmarshaller());
            response = invoke(request, responseHandler, executionContext);

            return response.getAwsResponse();

        } finally {

            endClientExecution(awsRequestMetrics, request, response);
        }
    }

    @Override
    public DescribeMLModelsResult describeMLModels() {
        return describeMLModels(new DescribeMLModelsRequest());
    }

    /**
     * <p>
     * Returns a <code>BatchPrediction</code> that includes detailed metadata,
     * status, and data file information for a <code>Batch Prediction</code>
     * request.
     * </p>
     * 
     * @param getBatchPredictionRequest
     * @return 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.
     * @throws ResourceNotFoundException
     *         A specified resource cannot be located.
     * @throws InternalServerException
     *         An error on the server occurred when trying to process a request.
     * @sample AmazonMachineLearning.GetBatchPrediction
     */
    @Override
    public GetBatchPredictionResult getBatchPrediction(
            GetBatchPredictionRequest getBatchPredictionRequest) {
        ExecutionContext executionContext = createExecutionContext(getBatchPredictionRequest);
        AWSRequestMetrics awsRequestMetrics = executionContext
                .getAwsRequestMetrics();
        awsRequestMetrics.startEvent(Field.ClientExecuteTime);
        Request<GetBatchPredictionRequest> request = null;
        Response<GetBatchPredictionResult> response = null;

        try {
            awsRequestMetrics.startEvent(Field.RequestMarshallTime);
            try {
                request = new GetBatchPredictionRequestMarshaller(
                        protocolFactory).marshall(super
                        .beforeMarshalling(getBatchPredictionRequest));
                // Binds the request metrics to the current request.
                request.setAWSRequestMetrics(awsRequestMetrics);
            } finally {
                awsRequestMetrics.endEvent(Field.RequestMarshallTime);
            }

            HttpResponseHandler<AmazonWebServiceResponse<GetBatchPredictionResult>> responseHandler = protocolFactory
                    .createResponseHandler(new JsonOperationMetadata()
                            .withPayloadJson(true)
                            .withHasStreamingSuccessResponse(false),
                            new GetBatchPredictionResultJsonUnmarshaller());
            response = invoke(request, responseHandler, executionContext);

            return response.getAwsResponse();

        } finally {

            endClientExecution(awsRequestMetrics, request, response);
        }
    }

    /**
     * <p>
     * Returns a <code>DataSource</code> that includes metadata and data file
     * information, as well as the current status of the <code>DataSource</code>
     * .
     * </p>
     * <p>
     * <code>GetDataSource</code> 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.
     * </p>
     * 
     * @param getDataSourceRequest
     * @return 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.
     * @throws ResourceNotFoundException
     *         A specified resource cannot be located.
     * @throws InternalServerException
     *         An error on the server occurred when trying to process a request.
     * @sample AmazonMachineLearning.GetDataSource
     */
    @Override
    public GetDataSourceResult getDataSource(
            GetDataSourceRequest getDataSourceRequest) {
        ExecutionContext executionContext = createExecutionContext(getDataSourceRequest);
        AWSRequestMetrics awsRequestMetrics = executionContext
                .getAwsRequestMetrics();
        awsRequestMetrics.startEvent(Field.ClientExecuteTime);
        Request<GetDataSourceRequest> request = null;
        Response<GetDataSourceResult> response = null;

        try {
            awsRequestMetrics.startEvent(Field.RequestMarshallTime);
            try {
                request = new GetDataSourceRequestMarshaller(protocolFactory)
                        .marshall(super.beforeMarshalling(getDataSourceRequest));
                // Binds the request metrics to the current request.
                request.setAWSRequestMetrics(awsRequestMetrics);
            } finally {
                awsRequestMetrics.endEvent(Field.RequestMarshallTime);
            }

            HttpResponseHandler<AmazonWebServiceResponse<GetDataSourceResult>> responseHandler = protocolFactory
                    .createResponseHandler(new JsonOperationMetadata()
                            .withPayloadJson(true)
                            .withHasStreamingSuccessResponse(false),
                            new GetDataSourceResultJsonUnmarshaller());
            response = invoke(request, responseHandler, executionContext);

            return response.getAwsResponse();

        } finally {

            endClientExecution(awsRequestMetrics, request, response);
        }
    }

    /**
     * <p>
     * Returns an <code>Evaluation</code> that includes metadata as well as the
     * current status of the <code>Evaluation</code>.
     * </p>
     * 
     * @param getEvaluationRequest
     * @return 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.
     * @throws ResourceNotFoundException
     *         A specified resource cannot be located.
     * @throws InternalServerException
     *         An error on the server occurred when trying to process a request.
     * @sample AmazonMachineLearning.GetEvaluation
     */
    @Override
    public GetEvaluationResult getEvaluation(
            GetEvaluationRequest getEvaluationRequest) {
        ExecutionContext executionContext = createExecutionContext(getEvaluationRequest);
        AWSRequestMetrics awsRequestMetrics = executionContext
                .getAwsRequestMetrics();
        awsRequestMetrics.startEvent(Field.ClientExecuteTime);
        Request<GetEvaluationRequest> request = null;
        Response<GetEvaluationResult> response = null;

        try {
            awsRequestMetrics.startEvent(Field.RequestMarshallTime);
            try {
                request = new GetEvaluationRequestMarshaller(protocolFactory)
                        .marshall(super.beforeMarshalling(getEvaluationRequest));
                // Binds the request metrics to the current request.
                request.setAWSRequestMetrics(awsRequestMetrics);
            } finally {
                awsRequestMetrics.endEvent(Field.RequestMarshallTime);
            }

            HttpResponseHandler<AmazonWebServiceResponse<GetEvaluationResult>> responseHandler = protocolFactory
                    .createResponseHandler(new JsonOperationMetadata()
                            .withPayloadJson(true)
                            .withHasStreamingSuccessResponse(false),
                            new GetEvaluationResultJsonUnmarshaller());
            response = invoke(request, responseHandler, executionContext);

            return response.getAwsResponse();

        } finally {

            endClientExecution(awsRequestMetrics, request, response);
        }
    }

    /**
     * <p>
     * Returns an <code>MLModel</code> that includes detailed metadata, and data
     * source information as well as the current status of the
     * <code>MLModel</code>.
     * </p>
     * <p>
     * <code>GetMLModel</code> provides results in normal or verbose format.
     * </p>
     * 
     * @param getMLModelRequest
     * @return 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.
     * @throws ResourceNotFoundException
     *         A specified resource cannot be located.
     * @throws InternalServerException
     *         An error on the server occurred when trying to process a request.
     * @sample AmazonMachineLearning.GetMLModel
     */
    @Override
    public GetMLModelResult getMLModel(GetMLModelRequest getMLModelRequest) {
        ExecutionContext executionContext = createExecutionContext(getMLModelRequest);
        AWSRequestMetrics awsRequestMetrics = executionContext
                .getAwsRequestMetrics();
        awsRequestMetrics.startEvent(Field.ClientExecuteTime);
        Request<GetMLModelRequest> request = null;
        Response<GetMLModelResult> response = null;

        try {
            awsRequestMetrics.startEvent(Field.RequestMarshallTime);
            try {
                request = new GetMLModelRequestMarshaller(protocolFactory)
                        .marshall(super.beforeMarshalling(getMLModelRequest));
                // Binds the request metrics to the current request.
                request.setAWSRequestMetrics(awsRequestMetrics);
            } finally {
                awsRequestMetrics.endEvent(Field.RequestMarshallTime);
            }

            HttpResponseHandler<AmazonWebServiceResponse<GetMLModelResult>> responseHandler = protocolFactory
                    .createResponseHandler(new JsonOperationMetadata()
                            .withPayloadJson(true)
                            .withHasStreamingSuccessResponse(false),
                            new GetMLModelResultJsonUnmarshaller());
            response = invoke(request, responseHandler, executionContext);

            return response.getAwsResponse();

        } finally {

            endClientExecution(awsRequestMetrics, request, response);
        }
    }

    /**
     * <p>
     * Generates a prediction for the observation using the specified
     * <code>ML Model</code>.
     * </p>
     * <note><title>Note</title>
     * <p>
     * Not all response parameters will be populated. Whether a response
     * parameter is populated depends on the type of model requested.
     * </p>
     * </note>
     * 
     * @param predictRequest
     * @return 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.
     * @throws ResourceNotFoundException
     *         A specified resource cannot be located.
     * @throws LimitExceededException
     *         The subscriber exceeded the maximum number of operations. This
     *         exception can occur when listing objects such as
     *         <code>DataSource</code>.
     * @throws InternalServerException
     *         An error on the server occurred when trying to process a request.
     * @throws PredictorNotMountedException
     *         The exception is thrown when a predict request is made to an
     *         unmounted <code>MLModel</code>.
     * @sample AmazonMachineLearning.Predict
     */
    @Override
    public PredictResult predict(PredictRequest predictRequest) {
        ExecutionContext executionContext = createExecutionContext(predictRequest);
        AWSRequestMetrics awsRequestMetrics = executionContext
                .getAwsRequestMetrics();
        awsRequestMetrics.startEvent(Field.ClientExecuteTime);
        Request<PredictRequest> request = null;
        Response<PredictResult> response = null;

        try {
            awsRequestMetrics.startEvent(Field.RequestMarshallTime);
            try {
                request = new PredictRequestMarshaller(protocolFactory)
                        .marshall(super.beforeMarshalling(predictRequest));
                // Binds the request metrics to the current request.
                request.setAWSRequestMetrics(awsRequestMetrics);
            } finally {
                awsRequestMetrics.endEvent(Field.RequestMarshallTime);
            }

            HttpResponseHandler<AmazonWebServiceResponse<PredictResult>> responseHandler = protocolFactory
                    .createResponseHandler(new JsonOperationMetadata()
                            .withPayloadJson(true)
                            .withHasStreamingSuccessResponse(false),
                            new PredictResultJsonUnmarshaller());
            response = invoke(request, responseHandler, executionContext);

            return response.getAwsResponse();

        } finally {

            endClientExecution(awsRequestMetrics, request, response);
        }
    }

    /**
     * <p>
     * Updates the <code>BatchPredictionName</code> of a
     * <code>BatchPrediction</code>.
     * </p>
     * <p>
     * You can use the <a>GetBatchPrediction</a> operation to view the contents
     * of the updated data element.
     * </p>
     * 
     * @param updateBatchPredictionRequest
     * @return 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.
     * @throws ResourceNotFoundException
     *         A specified resource cannot be located.
     * @throws InternalServerException
     *         An error on the server occurred when trying to process a request.
     * @sample AmazonMachineLearning.UpdateBatchPrediction
     */
    @Override
    public UpdateBatchPredictionResult updateBatchPrediction(
            UpdateBatchPredictionRequest updateBatchPredictionRequest) {
        ExecutionContext executionContext = createExecutionContext(updateBatchPredictionRequest);
        AWSRequestMetrics awsRequestMetrics = executionContext
                .getAwsRequestMetrics();
        awsRequestMetrics.startEvent(Field.ClientExecuteTime);
        Request<UpdateBatchPredictionRequest> request = null;
        Response<UpdateBatchPredictionResult> response = null;

        try {
            awsRequestMetrics.startEvent(Field.RequestMarshallTime);
            try {
                request = new UpdateBatchPredictionRequestMarshaller(
                        protocolFactory).marshall(super
                        .beforeMarshalling(updateBatchPredictionRequest));
                // Binds the request metrics to the current request.
                request.setAWSRequestMetrics(awsRequestMetrics);
            } finally {
                awsRequestMetrics.endEvent(Field.RequestMarshallTime);
            }

            HttpResponseHandler<AmazonWebServiceResponse<UpdateBatchPredictionResult>> responseHandler = protocolFactory
                    .createResponseHandler(new JsonOperationMetadata()
                            .withPayloadJson(true)
                            .withHasStreamingSuccessResponse(false),
                            new UpdateBatchPredictionResultJsonUnmarshaller());
            response = invoke(request, responseHandler, executionContext);

            return response.getAwsResponse();

        } finally {

            endClientExecution(awsRequestMetrics, request, response);
        }
    }

    /**
     * <p>
     * Updates the <code>DataSourceName</code> of a <code>DataSource</code>.
     * </p>
     * <p>
     * You can use the <a>GetDataSource</a> operation to view the contents of
     * the updated data element.
     * </p>
     * 
     * @param updateDataSourceRequest
     * @return 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.
     * @throws ResourceNotFoundException
     *         A specified resource cannot be located.
     * @throws InternalServerException
     *         An error on the server occurred when trying to process a request.
     * @sample AmazonMachineLearning.UpdateDataSource
     */
    @Override
    public UpdateDataSourceResult updateDataSource(
            UpdateDataSourceRequest updateDataSourceRequest) {
        ExecutionContext executionContext = createExecutionContext(updateDataSourceRequest);
        AWSRequestMetrics awsRequestMetrics = executionContext
                .getAwsRequestMetrics();
        awsRequestMetrics.startEvent(Field.ClientExecuteTime);
        Request<UpdateDataSourceRequest> request = null;
        Response<UpdateDataSourceResult> response = null;

        try {
            awsRequestMetrics.startEvent(Field.RequestMarshallTime);
            try {
                request = new UpdateDataSourceRequestMarshaller(protocolFactory)
                        .marshall(super
                                .beforeMarshalling(updateDataSourceRequest));
                // Binds the request metrics to the current request.
                request.setAWSRequestMetrics(awsRequestMetrics);
            } finally {
                awsRequestMetrics.endEvent(Field.RequestMarshallTime);
            }

            HttpResponseHandler<AmazonWebServiceResponse<UpdateDataSourceResult>> responseHandler = protocolFactory
                    .createResponseHandler(new JsonOperationMetadata()
                            .withPayloadJson(true)
                            .withHasStreamingSuccessResponse(false),
                            new UpdateDataSourceResultJsonUnmarshaller());
            response = invoke(request, responseHandler, executionContext);

            return response.getAwsResponse();

        } finally {

            endClientExecution(awsRequestMetrics, request, response);
        }
    }

    /**
     * <p>
     * Updates the <code>EvaluationName</code> of an <code>Evaluation</code>.
     * </p>
     * <p>
     * You can use the <a>GetEvaluation</a> operation to view the contents of
     * the updated data element.
     * </p>
     * 
     * @param updateEvaluationRequest
     * @return 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.
     * @throws ResourceNotFoundException
     *         A specified resource cannot be located.
     * @throws InternalServerException
     *         An error on the server occurred when trying to process a request.
     * @sample AmazonMachineLearning.UpdateEvaluation
     */
    @Override
    public UpdateEvaluationResult updateEvaluation(
            UpdateEvaluationRequest updateEvaluationRequest) {
        ExecutionContext executionContext = createExecutionContext(updateEvaluationRequest);
        AWSRequestMetrics awsRequestMetrics = executionContext
                .getAwsRequestMetrics();
        awsRequestMetrics.startEvent(Field.ClientExecuteTime);
        Request<UpdateEvaluationRequest> request = null;
        Response<UpdateEvaluationResult> response = null;

        try {
            awsRequestMetrics.startEvent(Field.RequestMarshallTime);
            try {
                request = new UpdateEvaluationRequestMarshaller(protocolFactory)
                        .marshall(super
                                .beforeMarshalling(updateEvaluationRequest));
                // Binds the request metrics to the current request.
                request.setAWSRequestMetrics(awsRequestMetrics);
            } finally {
                awsRequestMetrics.endEvent(Field.RequestMarshallTime);
            }

            HttpResponseHandler<AmazonWebServiceResponse<UpdateEvaluationResult>> responseHandler = protocolFactory
                    .createResponseHandler(new JsonOperationMetadata()
                            .withPayloadJson(true)
                            .withHasStreamingSuccessResponse(false),
                            new UpdateEvaluationResultJsonUnmarshaller());
            response = invoke(request, responseHandler, executionContext);

            return response.getAwsResponse();

        } finally {

            endClientExecution(awsRequestMetrics, request, response);
        }
    }

    /**
     * <p>
     * Updates the <code>MLModelName</code> and the <code>ScoreThreshold</code>
     * of an <code>MLModel</code>.
     * </p>
     * <p>
     * You can use the <a>GetMLModel</a> operation to view the contents of the
     * updated data element.
     * </p>
     * 
     * @param updateMLModelRequest
     * @return 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.
     * @throws ResourceNotFoundException
     *         A specified resource cannot be located.
     * @throws InternalServerException
     *         An error on the server occurred when trying to process a request.
     * @sample AmazonMachineLearning.UpdateMLModel
     */
    @Override
    public UpdateMLModelResult updateMLModel(
            UpdateMLModelRequest updateMLModelRequest) {
        ExecutionContext executionContext = createExecutionContext(updateMLModelRequest);
        AWSRequestMetrics awsRequestMetrics = executionContext
                .getAwsRequestMetrics();
        awsRequestMetrics.startEvent(Field.ClientExecuteTime);
        Request<UpdateMLModelRequest> request = null;
        Response<UpdateMLModelResult> response = null;

        try {
            awsRequestMetrics.startEvent(Field.RequestMarshallTime);
            try {
                request = new UpdateMLModelRequestMarshaller(protocolFactory)
                        .marshall(super.beforeMarshalling(updateMLModelRequest));
                // Binds the request metrics to the current request.
                request.setAWSRequestMetrics(awsRequestMetrics);
            } finally {
                awsRequestMetrics.endEvent(Field.RequestMarshallTime);
            }

            HttpResponseHandler<AmazonWebServiceResponse<UpdateMLModelResult>> responseHandler = protocolFactory
                    .createResponseHandler(new JsonOperationMetadata()
                            .withPayloadJson(true)
                            .withHasStreamingSuccessResponse(false),
                            new UpdateMLModelResultJsonUnmarshaller());
            response = invoke(request, responseHandler, executionContext);

            return response.getAwsResponse();

        } finally {

            endClientExecution(awsRequestMetrics, request, response);
        }
    }

    /**
     * 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.
     * <p>
     * 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.
     *
     * @param request
     *        The originally executed request
     *
     * @return The response metadata for the specified request, or null if none
     *         is available.
     */
    public ResponseMetadata getCachedResponseMetadata(
            AmazonWebServiceRequest request) {
        return client.getResponseMetadataForRequest(request);
    }

    /**
     * Normal invoke with authentication. Credentials are required and may be
     * overriden at the request level.
     **/
    private <X, Y extends AmazonWebServiceRequest> Response<X> invoke(
            Request<Y> request,
            HttpResponseHandler<AmazonWebServiceResponse<X>> responseHandler,
            ExecutionContext executionContext) {

        executionContext.setCredentialsProvider(CredentialUtils
                .getCredentialsProvider(request.getOriginalRequest(),
                        awsCredentialsProvider));

        return doInvoke(request, responseHandler, executionContext);
    }

    /**
     * Invoke with no authentication. Credentials are not required and any
     * credentials set on the client or request will be ignored for this
     * operation.
     **/
    private <X, Y extends AmazonWebServiceRequest> Response<X> anonymousInvoke(
            Request<Y> request,
            HttpResponseHandler<AmazonWebServiceResponse<X>> responseHandler,
            ExecutionContext executionContext) {

        return doInvoke(request, responseHandler, executionContext);
    }

    /**
     * Invoke the request using the http client. Assumes credentials (or lack
     * thereof) have been configured in the ExecutionContext beforehand.
     **/
    private <X, Y extends AmazonWebServiceRequest> Response<X> doInvoke(
            Request<Y> request,
            HttpResponseHandler<AmazonWebServiceResponse<X>> responseHandler,
            ExecutionContext executionContext) {
        request.setEndpoint(endpoint);
        request.setTimeOffset(timeOffset);

        HttpResponseHandler<AmazonServiceException> errorResponseHandler = protocolFactory
                .createErrorResponseHandler(new JsonErrorResponseMetadata());

        return client.execute(request, responseHandler, errorResponseHandler,
                executionContext);
    }

}