Machine learning (ML) teams use MLflow to manage their ML lifecycle effectively. Amazon SageMaker MLflow provides comprehensive ML experiment tracking and model management capabilities. However, many enterprises have existing infrastructure requirements that need HTTPS-based integrations rather than direct SDK usage.

Many organizations need to integrate Amazon SageMaker MLflow with their established systems while maintaining their security and infrastructure patterns. This integration challenge affects teams who can’t use the SDK directly because of corporate security policies, network restrictions, or legacy system constraints.

In this post, we demonstrate how to build a secure Flask-based MLflow proxy service that provides HTTPS access to Amazon SageMaker MLflow without requiring the MLflow SDK. This solution is for organizations undergoing cloud transformation who want to preserve their existing ML workflows while adopting cloud-native services.

A lightweight Flask-based MLflow proxy architecture provides secure integration between enterprise systems and Amazon SageMaker MLflow through three key components.

An AWS Application Load Balancer serves as the upstream router, providing the following:

Note: This implementation uses ALB, but you can alternatively use other routing solutions such as Nginx based on your requirements.