MLSUS-09: Archive or delete unnecessary training artifacts

Remove training artifacts that are unused and no longer required to limit wasted resources. Determine when you can archive training artifacts to more energy-efficient storage or safely delete them. 

Implementation plan

• Clean up unneeded training resources - Organize your ML experiments with SageMaker Experiments to clean up training resources you no longer need.

• Reduce the volume of logs you keep - By default, CloudWatch retains logs indefinitely. By setting limited retention time for your notebooks and training logs, you’ll avoid the environmental impact of unnecessary log storage.

Documents

• Clean Up Amazon SageMaker Experiment Resources

• Change log data retention in CloudWatch Logs

Blogs

• Optimize AI/ML workloads for sustainability: Part 2, model development

• Clean up Your Container Images with Amazon ECR Lifecycle Policies

Metrics

• Measure and optimize the total size of your Amazon S3 buckets and storage class distribution, using Amazon S3 Storage Lens

• Measure and optimize the size your of CloudWatch log groups