Build a custom portal with embedded Amazon SageMaker AI MLflow Apps

This article explains how to build a custom portal embedding Amazon SageMaker AI MLflow Apps using React and Flask, enabling ML teams secure, bookmarkable access to experiment tracking.

• Deploy React dashboard with embedded MLflow UI via iframe backed by Flask reverse proxy
• Flask proxy handles AWS SigV4 authentication, eliminating need for presigned URLs or console access
• Application Load Balancer provides single entry point with HTTPS termination and DNS integration
• Architecture includes four CDK stacks: networking, SageMaker domain, MLflow app, and Flask application
• Users authenticate once through SSO-integrated portal, access MLflow alongside other internal tools
• CI/CD pipelines interact with MLflow REST APIs programmatically through proxy endpoint
• Solution includes deployment script, validation steps, and cleanup procedures
• Production recommendations: add HTTPS, CloudWatch monitoring, rate limiting, and AWS WAF protection

This solution reduces onboarding time, simplifies access management, and provides consistent experience across internal tools for growing ML teams.