aws-java-sdk-sagemaker/src/main/java/com/amazonaws/services/sagemaker/AmazonSageMakerClient.java
@Override
    public CreateHyperParameterTuningJobResult createHyperParameterTuningJob(CreateHyperParameterTuningJobRequest request) {
        request = beforeClientExecution(request);
        return executeCreateHyperParameterTuningJob(request);
    }
/**
     * <p>
     * Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many
     * training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that
     * you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured
     * by an objective metric that you choose.
     * </p>
     * 
     * @param createHyperParameterTuningJobRequest
     * @return Result of the CreateHyperParameterTuningJob operation returned by the service.
     * @throws ResourceInUseException
     *         Resource being accessed is in use.
     * @throws ResourceLimitExceededException
     *         You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
     *         created.
     * @sample AmazonSageMaker.CreateHyperParameterTuningJob
     * @see <a href="http://docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/CreateHyperParameterTuningJob"
     *      target="_top">AWS API Documentation</a>
     */

Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many
training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that
you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured
by an objective metric that you choose.

@param createHyperParameterTuningJobRequest
@return Result of the CreateHyperParameterTuningJob operation returned by the service.
@throws ResourceInUseException
Resource being accessed is in use.
@throws ResourceLimitExceededException
You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs
created.
Starts a hyperparameter tuning job . A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify . It then chooses the hyperparameter values that result in a model that performs the best as measured by an objective metric that you choose . @param createHyperParameterTuningJobRequest @return Result of the CreateHyperParameterTuningJob operation returned by the service . @throws ResourceInUseException Resource being accessed is in use . @throws ResourceLimitExceededException You have exceeded an Amazon SageMaker resource limit . For example you might have too many training jobs created .
@Override
    public CreateHyperParameterTuningJobResult createHyperParameterTuningJob(CreateHyperParameterTuningJobRequest request) {
        request = beforeClientExecution(request);
        return executeCreateHyperParameterTuningJob(request);
    }
@ Override public CreateHyperParameterTuningJobResult createHyperParameterTuningJob ( CreateHyperParameterTuningJobRequest request ) { request = beforeClientExecution ( request ) ; return executeCreateHyperParameterTuningJob ( request ) ; }
<p>
 Starts a hyperparameter tuning job.
<p > Starts a hyperparameter tuning job .