Step 7: Clean Up - Amazon SageMaker

Step 7: Clean Up

To avoid incurring unnecessary charges, use the AWS Management Console to delete the endpoints and resources that you created while running the exercises.

Note

If you plan to explore other exercises in this guide, you might want to keep some of these resources, such as your notebook instance, S3 bucket, and IAM role.

  1. Open the Amazon SageMaker console at https://console.aws.amazon.com/sagemaker/ and delete the following resources:

    • The endpoint. Deleting the endpoint also deletes the ML compute instance or instances that support it.

      1. Under Inference, choose Endpoints.

      2. Choose the endpoint that you created in the example, choose Actions, and then choose Delete.

    • The endpoint configuration.

      1. Under Inference, choose Endpoint configurations.

      2. Choose the endpoint configuration that you created in the example, choose Actions, and then choose Delete.

    • The model.

      1. Under Inference, choose Models.

      2. Choose the model that you created in the example, choose Actions, and then choose Delete.

    • The notebook instance. Before deleting the notebook instance, stop it.

      1. Under Notebook, choose Notebook instances.

      2. Choose the notebook instance that you created in the example, choose Actions, and then choose Stop. The notebook instance takes several minutes to stop. When the Status changes to Stopped, move on to the next step.

      3. Choose Actions, and then choose Delete.

  2. Open the Amazon S3 console at https://console.aws.amazon.com/s3/, and then delete the bucket that you created for storing model artifacts and the training dataset.

  3. Open the Amazon CloudWatch console at https://console.aws.amazon.com/cloudwatch/, and then delete all of the log groups that have names starting with /aws/sagemaker/.