async fn create_ml_model(
        &self,
        input: CreateMLModelInput,
    ) -> Result<CreateMLModelOutput, RusotoError<CreateMLModelError>> {
        let mut request = self.new_signed_request("POST", "/");
        request.add_header("x-amz-target", "AmazonML_20141212.CreateMLModel");
        let encoded = serde_json::to_string(&input).unwrap();
        request.set_payload(Some(encoded));

        let response = self
            .sign_and_dispatch(request, CreateMLModelError::from_response)
            .await?;
        let mut response = response;
        let response = response.buffer().await.map_err(RusotoError::HttpDispatch)?;
        proto::json::ResponsePayload::new(&response).deserialize::<CreateMLModelOutput, _>()
    }
<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>
rusoto/services/machinelearning/src/generated.rs