
Build a custom portal with embedded Amazon SageMaker AI MLflow Apps
Quick Answer
This article guides you through building a custom portal using Amazon SageMaker and MLflow Apps, integrating a React front end with a Flask reverse proxy for AWS SigV4 authentication.
Quick Take
It covers deployment via AWS CDK, validation, security considerations, and cleanup procedures.
Key Points
- Integrate React front end with Flask for AWS SigV4 authentication.
- Deploy the entire stack using AWS Cloud Development Kit (CDK).
- Validate deployment and review security considerations.
- Includes cleanup procedures for efficient resource management.
Article Excerpt
From source RSS / original summaryIn this post, you learn how to build a custom portal with embedded SageMaker AI MLflow Apps UI. You walk through the architecture pattern behind a React front end paired with a Flask reverse proxy that handles AWS Signature Version 4 (SigV4) authentication, deploy the entire stack through the AWS Cloud Development Kit (AWS CDK), validate the deployment, and review security considerations and cleanup procedures.
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from AWS Machine Learning
See more →
Building an agentic app deployer with Amazon Bedrock and AWS Lambda
PDI Technologies developed PDI Brew, enabling non-technical employees to create web applications on AWS without developer involvement, leveraging Amazon Bedrock for AI capabilities. This agentic app deployer streamlines internal tool delivery, removing traditional bottlenecks in deployment pipelines.

