code: @Override
      public CreateMLModelResult createMLModel(CreateMLModelRequest request) {
          request = beforeClientExecution(request);
          return executeCreateMLModel(request);
      }
docs: <p>
  Creates a new <code>MLModel</code> using the <code>DataSource</code> and the recipe as information sources.
  </p>
  <p>
  An <code>MLModel</code> is nearly immutable. Users can update only 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> has been created and ready is for use, Amazon ML sets the status to
  <code>COMPLETED</code>.
  </p>
  <p>
  You can use the <code>GetMLModel</code> operation to check the progress of the <code>MLModel</code> during the
  creation operation.
  </p>
  <p>
  <code>CreateMLModel</code> requires a <code>DataSource</code> with computed statistics, which can be created by
  setting <code>ComputeStatistics</code> to <code>true</code> in <code>CreateDataSourceFromRDS</code>,
  <code>CreateDataSourceFromS3</code>, or <code>CreateDataSourceFromRedshift</code> 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