Creating a Batch Prediction - Amazon Machine Learning

We are no longer updating the Amazon Machine Learning service or accepting new users for it. This documentation is available for existing users, but we are no longer updating it. For more information, see What is Amazon Machine Learning.

Creating a Batch Prediction

To create a batch prediction, you create a BatchPrediction object using either the Amazon Machine Learning (Amazon ML) console or API. A BatchPrediction object describes a set of predictions that Amazon ML generates by using your ML model and a set of input observations. When you create a BatchPrediction object, Amazon ML starts an asynchronous workflow that computes the predictions.

You must use the same schema for the datasource that you use to obtain batch predictions and the datasource that you used to train the ML model that you query for predictions. The one exception is that the datasource for a batch prediction doesn't need to include the target attribute because Amazon ML predicts the target. If you provide the target attribute, Amazon ML ignores its value.

Creating a Batch Prediction (Console)

To create a batch prediction using the Amazon ML console, use the Create Batch Prediction wizard.

To create a batch prediction (console)
  1. Sign in to the AWS Management Console and open the Amazon Machine Learning console at

  2. On the Amazon ML dashboard, under Objects, choose Create new..., and then choose Batch prediction.

  3. Choose the Amazon ML model that you want to use to create the batch prediction.

  4. To confirm that you want to use this model, choose Continue.

  5. Choose the datasource that you want to create predictions for. The datasource must have the same schema as your model, although it doesn't need to include the target attribute.

  6. Choose Continue.

  7. For S3 destination, type the name of your S3 bucket.

  8. Choose Review.

  9. Review your settings and choose Create batch prediction.

Creating a Batch Prediction (API)

To create a BatchPrediction object using the Amazon ML API, you must provide the following parameters:

Datasource ID

The ID of the datasource that points to the observations for which you want predictions. For example, if you want predictions for data in a file called s3://examplebucket/input.csv, you would create a datasource object that points to the data file, and then pass in the ID of that datasource with this parameter.

BatchPrediction ID

The ID to assign to the batch prediction.

ML Model ID

The ID of the ML model that Amazon ML should query for the predictions.

Output Uri

The URI of the S3 bucket in which to store the output of the prediction. Amazon ML must have permissions to write data to this bucket.

The OutputUri parameter must refer to an S3 path that ends with a forward slash ('/') character, as shown in the following example:


For information about configuring S3 permissions , see Granting Amazon ML Permissions to Output Predictions to Amazon S3.

(Optional) BatchPrediction Name

(Optional) A human-readable name for your batch prediction.