AWS has introduced a solution that allows organizations to embed Amazon SageMaker AI MLflow Apps into their internal portals, enabling a persistent, bookmarkable URL for MLflow without requiring presigned URLs or direct AWS Management Console access. This approach addresses challenges faced by teams with dozens of data scientists, as distributing presigned URLs scales poorly and granting individual console access adds operational overhead. The solution enables teams to integrate MLflow experiment tracking alongside other internal applications through a single bookmarkable URL, reducing onboarding time for new members and simplifying access management. The system allows data scientists to access a consistent experience across internal tools while maintaining secure authentication through single sign-on (SSO) integration. The architecture combines a React front end, a Flask reverse proxy handling AWS SigV4 authentication, and a deployment via AWS Cloud Development Kit (CDK). This setup ensures that continuous integration and delivery (CI/CD) pipelines can interact with ML,flow REST APIs programmatically through a unified proxy endpoint. *Source: [awsml](https://aws.amazon.com/blogs/machine-learning/build-a-custom-portal-with-embedded-amazon-sagemaker-ai-mlflow-apps/)*