fn create_transform_job(
        &self,
        input: CreateTransformJobRequest,
    ) -> RusotoFuture<CreateTransformJobResponse, CreateTransformJobError> {
        let mut request = SignedRequest::new("POST", "sagemaker", &self.region, "/");

        request.set_content_type("application/x-amz-json-1.1".to_owned());
        request.add_header("x-amz-target", "SageMaker.CreateTransformJob");
        let encoded = serde_json::to_string(&input).unwrap();
        request.set_payload(Some(encoded.into_bytes()));

        self.client.sign_and_dispatch(request, |response| {
            if response.status.is_success() {
                Box::new(response.buffer().from_err().map(|response| {
                    let mut body = response.body;

                    if body.is_empty() || body == b"null" {
                        body = b"{}".to_vec();
                    }

                    serde_json::from_str::<CreateTransformJobResponse>(
                        String::from_utf8_lossy(body.as_ref()).as_ref(),
                    )
                    .unwrap()
                }))
            } else {
                Box::new(
                    response
                        .buffer()
                        .from_err()
                        .and_then(|response| Err(CreateTransformJobError::from_response(response))),
                )
            }
        })
    }
/// <p>Starts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify.</p> <p>To perform batch transformations, you create a transform job and use the data that you have readily available.</p> <p>In the request body, you provide the following:</p> <ul> <li> <p> <code>TransformJobName</code> - Identifies the transform job. The name must be unique within an AWS Region in an AWS account.</p> </li> <li> <p> <code>ModelName</code> - Identifies the model to use. <code>ModelName</code> must be the name of an existing Amazon SageMaker model within an AWS Region in an AWS account.</p> </li> <li> <p> <code>TransformInput</code> - Describes the dataset to be transformed and the Amazon S3 location where it is stored.</p> </li> <li> <p> <code>TransformOutput</code> - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.</p> </li> <li> <p> <code>TransformResources</code> - Identifies the ML compute instances for the transform job.</p> </li> </ul> <p> For more information about how batch transformation works Amazon SageMaker, see <a href="http://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform.html">How It Works</a>. </p>
Starts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify. To perform batch transformations, you create a transform job and use the data that you have readily available. In the request body, you provide the following:    TransformJobName - Identifies the transform job. The name must be unique within an AWS Region in an AWS account.    ModelName - Identifies the model to use. ModelName must be the name of an existing Amazon SageMaker model within an AWS Region in an AWS account.    TransformInput - Describes the dataset to be transformed and the Amazon S3 location where it is stored.    TransformOutput - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.    TransformResources - Identifies the ML compute instances for the transform job.    For more information about how batch transformation works Amazon SageMaker, see How It Works.
Starts a transform job . A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify . To perform batch transformations you create a transform job and use the data that you have readily available . In the request body you provide the following : TransformJobName - Identifies the transform job . The name must be unique within an AWS Region in an AWS account . ModelName - Identifies the model to use . ModelName must be the name of an existing Amazon SageMaker model within an AWS Region in an AWS account . TransformInput - Describes the dataset to be transformed and the Amazon S3 location where it is stored . TransformOutput - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job . TransformResources - Identifies the ML compute instances for the transform job . For more information about how batch transformation works Amazon SageMaker see How It Works .
fn create_transform_job(
        &self,
        input: CreateTransformJobRequest,
    ) -> RusotoFuture<CreateTransformJobResponse, CreateTransformJobError> {
        let mut request = SignedRequest::new("POST", "sagemaker", &self.region, "/");
        request.set_content_type("application/x-amz-json-1.1".to_owned());
        request.add_header("x-amz-target", "SageMaker.CreateTransformJob");
        let encoded = serde_json::to_string(&input).unwrap();
        request.set_payload(Some(encoded.into_bytes()));
        self.client.sign_and_dispatch(request, |response| {
            if response.status.is_success() {
                Box::new(response.buffer().from_err().map(|response| {
                    let mut body = response.body;
                    if body.is_empty() || body == b"null" {
                        body = b"{}".to_vec();
                    }
                    serde_json::from_str::<CreateTransformJobResponse>(
                        String::from_utf8_lossy(body.as_ref()).as_ref(),
                    )
                    .unwrap()
                }))
            } else {
                Box::new(
                    response
                        .buffer()
                        .from_err()
                        .and_then(|response| Err(CreateTransformJobError::from_response(response))),
                )
            }
        })
    }
fn create_transform_job ( & self , input : CreateTransformJobRequest , ) -> RusotoFuture < CreateTransformJobResponse , CreateTransformJobError > { let mut request = SignedRequest :: new ( " " , " " , & self . region , " " ) ; request . set_content_type ( " " . to_owned ( ) ) ; request . add_header ( " " , " " ) ; let encoded = serde_json :: to_string ( & input ) . unwrap ( ) ; request . set_payload ( Some ( encoded . into_bytes ( ) ) ) ; self . client . sign_and_dispatch ( request , | response | { if response . status . is_success ( ) { Box :: new ( response . buffer ( ) . from_err ( ) . map ( | response | { let mut body = response . body ; if body . is_empty ( ) || body == b" " { body = b" " . to_vec ( ) ; } serde_json :: from_str :: < CreateTransformJobResponse > ( String :: from_utf8_lossy ( body . as_ref ( ) ) . as_ref ( ) , ) . unwrap ( ) } ) ) } else { Box :: new ( response . buffer ( ) . from_err ( ) . and_then ( | response | Err ( CreateTransformJobError :: from_response ( response ) ) ) , ) } } ) }
<p>Starts a transform job.
<p > Starts a transform job .
&self
input
CreateTransformJobRequest
&self
input
CreateTransformJobRequest