AWS SageMaker: Embedding MLflow UI in a Custom Portal via React

AWS published a detailed guide on embedding SageMaker MLflow Apps UI directly into a custom web application. The documentation describes the complete architecture (React + Flask + AWS CDK), the deployment process, and key security aspects for enterprise scenarios.

Why Embed MLflow in Your Own Portal

MLflow is a popular tool for tracking ML experiments, versioning models, and managing artifacts. Data scientists and engineers use it constantly.

The problem: when a company has many systems, employees waste time switching between interfaces. SageMaker MLflow Apps allows running MLflow in AWS cloud. Previously, embedding this service into a corporate portal was a complex task — it required proper AWS authentication handling, request proxying, and access management. AWS now presented a ready-made solution that can be copied and adapted to your needs.

Architecture: React, Flask, and SigV4

The solution consists of three layers. The React frontend displays the MLflow interface, manages user sessions, and sends requests to the backend. A Flask reverse proxy intercepts requests from the browser, adds an AWS Signature Version 4 (SigV4) signature to each request, proxies it to SageMaker MLflow, and returns the response to the client.

• React application manages UI and user authorization
• Flask handler adds SigV4 signatures to requests
• AWS SageMaker MLflow processes model and experiment data
• AWS CDK automatically creates all infrastructure
• CloudTrail logs all operations for audit

The entire stack is deployed via AWS CDK (Cloud Development Kit) — an Infrastructure as Code tool that automatically creates all necessary AWS resources from code in Python or TypeScript.

How Security Works

SigV4 is a digital signature mechanism for AWS API requests. The Flask server has IAM credentials and signs each request before sending it to SageMaker. The browser itself doesn't see AWS keys — they are stored only on the Flask server.

"The reverse proxy architecture allows centralizing authentication and maintaining full control over access,"

AWS describes the approach in its documentation. This makes it possible to hide credentials from the client, control access through IAM policies, log all requests in CloudTrail, and divide permissions among users. Even if a user opens the browser's DevTools, they won't find secret AWS keys.

What This Means

AWS has simplified MLflow migration to the enterprise. Now large companies can integrate SageMaker MLflow into their corporate portal — a single application through which employees access all tools. The guide includes complete source code, step-by-step deployment instructions, and a checklist for deployment verification.