How To Delete Sagemaker

Sagemaker Cleanup: Declutter Your AWS Machine Learning Environment

Amazon SageMaker is a powerful tool for building, training, and deploying machine learning models. But like any workspace, things can get cluttered over time. Old experiments, unused models, and inactive resources can lead to unnecessary costs and complicate your workflow.

Here’s a guide to cleaning up your SageMaker environment and keeping it organized:

1. Identify resources for deletion:

  • Models: Review your list of models. Are any outdated or no longer used? Delete them to free up storage space and simplify management.
  • Endpoints: Do you have inactive endpoints that are still incurring charges? Terminate them to avoid unnecessary costs.
  • Notebook instances: Stop and delete any notebook instances you’re not actively using. They can rack up charges even when idle.
  • Training jobs: Check for old training jobs that are complete or failed. Delete them to clean up your job history.
  • Data buckets: Look for S3 buckets associated with SageMaker that you no longer need. Empty or delete them to optimize storage costs.

2. Utilize the SageMaker console:

The SageMaker console provides a user-friendly interface for managing your resources. You can easily view lists of models, endpoints, notebook instances, and training jobs. Each resource has an associated “Delete” button for quick removal.

3. Leverage the AWS CLI:

For more advanced users, the AWS CLI offers a powerful command-line interface for managing SageMaker resources. You can use commands like sagemaker-delete-model and sagemaker-delete-endpoint to delete specific resources.

4. Automate cleanup with CloudFormation:

CloudFormation templates can automate the deletion of SageMaker resources based on predefined criteria. This is a great option for organizations with large or complex SageMaker environments.

5. Consider cost optimization services:

AWS offers services like AWS Cost Optimizer and AWS Budgets that can help you identify and eliminate unnecessary spending on SageMaker resources. These services can provide recommendations for optimizing your SageMaker environment and reducing costs.


  • Always double-check before deleting resources, especially models and endpoints that might be used by other applications.
  • Back up important data before deleting any resources.
  • Consider setting up retention policies for your SageMaker resources to automate cleanup on a regular basis.

By following these tips, you can effectively declutter your SageMaker environment, optimize costs, and maintain a clean and organized workspace for your machine learning projects.

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