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Amazon Machine Learning
Developer Guide (Version Latest)

Reviewing Batch Prediction Metrics

After Amazon Machine Learning (Amazon ML) creates a batch prediction, it provides two metrics: Records seen and Records failed to process. Records seen tells you how many records Amazon ML looked at when it ran your batch prediction. Records failed to process tells you how many records Amazon ML could not process.

To allow Amazon ML to process failed records, check the formatting of the records in the data used to create your datasource, and make sure that all of the required attributes are present and all of the data is correct. After fixing your data, you can either recreate your batch prediction, or create a new datasource with the failed records, and then create a new batch prediction using the new datasource.

Reviewing Batch Prediction Metrics (Console)

To see the metrics in the Amazon ML console, open the Batch prediction summary page and look in the Processed Info section.

Reviewing Batch Prediction Metrics and Details (API)

You can use the Amazon ML APIs to retrieve details about BatchPrediction objects, including the record metrics. Amazon ML provides the following batch prediction API calls:

  • CreateBatchPrediction

  • UpdateBatchPrediction

  • DeleteBatchPrediction

  • GetBatchPrediction

  • DescribeBatchPredictions

For more information, see the Amazon ML API Reference.