System Limits - 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.

System Limits

In order to provide a robust, reliable service, Amazon ML imposes certain limits on the requests you make to the system. Most ML problems fit easily within these constraints. However, if you do find that your use of Amazon ML is being restricted by these limits, you can contact AWS customer service and request to have a limit raised. For example, you might have a limit of five for the number of jobs that you can run simultaneously. If you find that you often have jobs queued that are waiting for resources because of this limit, then it probably makes sense to raise that limit for your account.

The following table shows default per-account limits in Amazon ML. Not all of these limits can be raised by AWS customer service.

Limit Type

System Limit

Size of each observations

100 KB

Size of training data *

100 GB

Size of batch prediction input

1 TB

Size of batch prediction input (number of records)

100 million

Number of variables in a data file (schema)

1,000

Recipe complexity (number of processed output variables)

10,000

TPS for each real-time prediction endpoint

 200

Total TPS for all real-time prediction endpoints

10,000

Total RAM for all real-time prediction endpoints

10 GB

Number of simultaneous jobs

25

Longest run time for any job

7 days

Number of classes for multiclass ML models

100

ML model size

Minimum of 1 MB, maximum of 2 GB

Number of tags per object

50

  • The size of your data files is limited to ensure that jobs finish in a timely manner. Jobs that have been running for more than seven days will be automatically terminated, resulting in a FAILED status.