Create real-time, personalized movie recommendations with Amazon Personalize

Introduction

Introduction

Module 1

Background and setup

Module 2

Download and prepare dataset

Module 3

Import dataset

Module 4

Create solution

Module 5

Create campaign

Clean up

Clean up and next steps

Clean up and next steps

In the previous modules, you created a personalized, real-time movie title recommendation model. To build this, you used:

These tools helped you create user recommendations with machine learning.

In the following steps, you clean up the resources you created in this lab.

Time to Complete Module: 10 Minutes

  • Step 1. Delete the Amazon Personalize resources

    Step 1. Delete the Amazon Personalize resources

    Run the following code to remove your Amazon Personalize resources:

    # Delete the campaign
personalize.delete_campaign(campaignArn=campaign_arn)
time.sleep(300)
print("delete_campaign done")
# Delete the solution
personalize.delete_solution(solutionArn=solution_arn)
time.sleep(60)
print("delete_solution done")
# Delete the interaction dataset
personalize.delete_dataset(datasetArn=interactions_dataset_arn)
time.sleep(60)
print("delete_dataset done")
# Delete the schema
personalize.delete_schema(schemaArn=interaction_schema_arn)
time.sleep(60)
print("delete_schema done")
# Delete the dataset group
personalize.delete_dataset_group(datasetGroupArn = dataset_group_arn)
time.sleep(60)
print("delete_dataset_group done")

    You should see the following output in your terminal:

    delete_campaign done
delete_solution done
delete_dataset done
delete_schema done
delete_dataset_group done

  • Step 2. Delete Amazon S3 bucket and IAM policies

    Step 2. Delete Amazon S3 bucket and IAM policies

    Next, clean up the Amazon S3 bucket and IAM policies.

    # Empty S3 Bucket
boto3.Session().resource('s3').Bucket(bucket_name).Object(interactions_filename).delete()
# IAM policies should also be removed
iam = boto3.client("iam")
iam.detach_role_policy(PolicyArn="arn:aws:iam::aws:policy/AmazonS3FullAccess", RoleName=role_name)
iam.detach_role_policy(PolicyArn="arn:aws:iam::aws:policy/service-role/AmazonPersonalizeFullAccess",RoleName=role_name)
iam.detach_role_policy(PolicyArn="arn:aws:iam::aws:policy/service-role/IAMFullAccess",RoleName=role_name)
iam.delete_role(RoleName=role_name)

  • Step 3. Delete SageMaker Notebook instance

    Step 3. Delete SageMaker Notebook instance

    Next, stop and delete your Amazon SageMaker Notebook instance.

    1. Open the SageMaker console.
    2. Under Notebooks, choose Notebook instances.
    3. Choose the notebook instance that you created for this tutorial, then choose Actions, Stop. The notebook instance takes up to several minutes to stop. When Status changes to Stopped, move on to the next step.
    4. Choose Actions, then Delete.
    5. Choose Delete.