async fn create_batch_prediction(
        &self,
        input: CreateBatchPredictionInput,
    ) -> Result<CreateBatchPredictionOutput, RusotoError<CreateBatchPredictionError>> {
        let mut request = self.new_signed_request("POST", "/");
        request.add_header("x-amz-target", "AmazonML_20141212.CreateBatchPrediction");
        let encoded = serde_json::to_string(&input).unwrap();
        request.set_payload(Some(encoded));

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