Streamline external access to Amazon SageMaker MLflow using a REST API proxy

This article explains how to build a secure Flask-based REST API proxy for accessing Amazon SageMaker MLflow without requiring the MLflow SDK, enabling HTTPS integration with enterprise systems.

• Flask proxy service intercepts HTTPS requests and transforms them into authenticated AWS API calls
• Application Load Balancer routes traffic to the proxy service for MLflow UI and REST API requests
• Supports both MLflow Tracking Server and serverless MLflow App deployment modes
• IAM authentication and URL pre-signing ensure secure access to SageMaker MLflow
• Solution deployed via AWS CDK with four stacks: networking, SageMaker domain, MLflow, and Flask app
• Includes validation steps using curl commands to test MLflow REST API endpoints
• Production security recommendations: CloudWatch monitoring, rate limiting, internal ALB, HTTPS termination

This solution enables organizations to integrate SageMaker MLflow with existing enterprise infrastructure while maintaining security compliance and reducing implementation complexity.