Amazon SageMaker JumpStart is a comprehensive machine learning platform that provides users with access to a wide range of pre-built machine learning models and algorithms. These models, known as foundation models, cover a variety of use cases and industries, making it easier for users to quickly deploy machine learning solutions without the need for extensive coding or data science expertise.
One of the key features of Amazon SageMaker JumpStart is the ability to create private hubs, which allow organizations to manage access to foundation models within their own AWS accounts. This can be particularly useful for companies that want to restrict access to certain models or ensure that sensitive data is not shared outside of the organization.
To manage access to Amazon SageMaker JumpStart foundation models using private hubs on Amazon Web Services, follow these steps:
1. Set up a private hub: To create a private hub, navigate to the Amazon SageMaker console and select “Private hubs” from the left-hand menu. Click on the “Create private hub” button and follow the prompts to set up your private hub. You can choose a name for your hub and specify which AWS accounts will have access to it.
2. Add foundation models to your private hub: Once your private hub is set up, you can start adding foundation models to it. Navigate to the “Foundation models” section of the Amazon SageMaker console and select the models you want to add to your private hub. Click on the “Add to private hub” button and choose the private hub you created in step 1.
3. Manage access controls: To control who has access to your private hub and its foundation models, you can set up IAM policies within your AWS account. These policies allow you to specify which users or roles have permission to view, deploy, or modify the models within your private hub.
4. Monitor usage and activity: Once your private hub is up and running, it’s important to monitor usage and activity to ensure that only authorized users are accessing the foundation models. You can use AWS CloudTrail logs to track who is accessing your private hub and what actions they are performing.
By following these steps, you can effectively manage access to Amazon SageMaker JumpStart foundation models using private hubs on Amazon Web Services. This allows you to control who has access to your machine learning models and ensure that sensitive data remains secure within your organization.