Managing ModelOps workflows can be complex and time-consuming. If you’ve struggled with setting up project templates for your data science team, you know that the previous approach using AWS Service Catalog required configuring portfolios, products, and managing complex permissions—adding significant administrative overhead before your team could start building machine learning (ML) pipelines.
Amazon SageMaker AI Projects now offers an easier path: Amazon S3 based templates. With this new capability, you can store AWS CloudFormation templates directly in Amazon Simple Storage Service (Amazon S3) and manage their entire lifecycle using familiar S3 features such as versioning, lifecycle policies, and S3 Cross-Region replication. This means you can provide your data science team with secure, version-controlled, automated project templates with significantly less overhead.
This post explores how you can use Amazon S3-based templates to simplify ModelOps…