Build an Amazon SageMaker Model Registry approval and promotion workflow with human intervention

This article discusses a solution to build an automated workflow for approving and promoting machine learning models with human intervention in Amazon SageMaker. The workflow enables a scalable process for the model development lifecycle.

Specifically, the article covers:

• Overview of the solution architecture using AWS services like Lambda, API Gateway, EventBridge, and SageMaker Model Registry
• Steps involved in the approval and promotion workflow from model creation to production
• Details on how EventBridge monitors model registration and triggers approval workflow
• Use of Lambda functions and API Gateway to enable human approvers to review and approve models
• Storage of model details like version, approved environment, and package in Parameter Store
• Post-inference notification to approvers for promoting model to next environment