Scalable Access Management for MLflow with Amazon SageMaker: A Custom Portal Solution

Introduction to Efficient Access Management for ML Teams

Solution Overview: Building a Custom Portal

Architecture and Request Workflow: How Components Interact

Deployment Walkthrough: Step-by-Step Guide

Clean Up: Managing Resources After Use

CDK Stack Details: Understanding the Layers of Architecture

Next Steps: Enhancing Your Portal’s Security and Functionality

Conclusion: Streamlining MLflow Access Through Custom Solutions

About the Authors: Meet Our Experts in Cloud Solutions

Building a Scalable MLflow Portal with Amazon SageMaker AI

As the landscape of machine learning (ML) evolves, so do the needs of ML teams. With the increasing number of data scientists collaborating on projects, effective access management becomes paramount. Distributing presigned URLs or granting individual access to the AWS Management Console may not be the best solutions for teams that are expanding. Instead, embedding Amazon SageMaker AI MLflow Apps into a custom, scalable portal can streamline access management while significantly improving the user experience.

Why a Custom Portal?

A custom portal allows data scientists to access their MLflow experiment tracking alongside other internal applications, all through a single bookmarkable URL. This approach reduces onboarding time for new team members, simplifies access management, and offers a consistent experience across internal tools. In this post, we’ll dive into how to build a custom portal that integrates the SageMaker AI MLflow Apps UI, providing your machine learning teams seamless access without relying on presigned URLs or individual management console access.

Solution Overview

The core architecture consists of:

1. Application Load Balancer (ALB)
2. React Frontend Portal
3. Flask Reverse Proxy Service
4. Amazon SageMaker AI MLflow Apps

Application Load Balancer (ALB)

The ALB serves as the first touchpoint for users, managing traffic and routing to the appropriate backend components. It is configured for HTTPS termination, seamlessly integrating with existing Single Sign-On (SSO) setups to provide a stable, public-facing URL.

React Frontend Portal

The React app provides a branded entry point, embedding the MLflow tracking UI in an iframe, while allowing organizational customization. It serves static files through the Flask proxy, ensuring a smooth user experience.

Flask Reverse Proxy Service

The Flask reverse proxy handles the authentication process. It intercepts incoming requests, signs them with AWS SigV4 using temporary credentials, and forwards these requests to the Amazon SageMaker AI MLflow Apps endpoint. This process ensures users never directly manage AWS credentials.

Amazon SageMaker AI MLflow Apps

Fully managed by AWS, the SageMaker AI MLflow Apps provide experiment tracking capabilities including logs, metrics, parameters, and artifacts. This architecture supports secure communication while maintaining compatibility with enterprise-level portals.

Architecture and Request Workflow

When a user accesses the portal:

1. The ALB routes the request to the Flask proxy.
2. The proxy serves the React dashboard and loads the MLflow UI in an iframe.
3. Any MLflow requests from this iframe are sent back through the Flask proxy, which signs them and forwards them to the MLflow Apps endpoint.
4. The proxy rewrites MLflow URLs and strips unnecessary headers, ensuring the interface renders correctly.

Implementation Walkthrough

Prerequisites

Before deploying this solution, ensure you have:

• An AWS account
• AWS CLI v2 installed
• Python 3.13 or later
• AWS CDK v2 installed
• Node.js 18.x or later
• Necessary IAM permissions

Deployment Steps

1. Clone the Repository

   git clone https://github.com/aws-samples/sample-sagemaker-mlflow-embedded-ui.git
   cd sample-sagemaker-mlflow-embedded-ui
   npm install

2. Set Environment Variables

   export CDK_DEFAULT_ACCOUNT=your_account_id
   export CDK_DEFAULT_REGION=your_region

3. Deploy Resources
   Follow the guided setup scripts to deploy the AWS resources.

4. Set Up the Flask Proxy
   Access the Amazon EC2 instance and install dependencies.

5. Validate the Deployment
   Interact with the MLflow REST APIs to confirm the proper setup.

Cleaning Up

To avoid ongoing charges, run the cleanup script provided in the repository. Additionally, manually delete the MLflow artifacts S3 bucket if it’s no longer needed.

Next Steps

For enhanced production environments:

• Implement AWS CloudWatch for monitoring.
• Enable HTTPS termination at the ALB level.
• Configure rate limiting to protect against DDoS attacks using AWS WAF.

Conclusion

By implementing a custom portal for the Amazon SageMaker AI MLflow Apps UI embedded within your organization’s infrastructure, you empower your ML team with simplified access management and a consistent user experience. With this guide, you’ll not only streamline your ML workflows but also provide your team with the tools they need to succeed.

About the Authors

• Manish Garg: Lead Consultant, AWS Professional Services.
• Ram Yennapusa: Senior Delivery Consultant, AWS.
• Ashish Bhatt: Senior Delivery Consultant, AWS.

For more detailed instructions, implementation steps, and code samples, check the repository and enhance your ML infrastructure today!