Announcing the general availability of fully managed MLflow on Amazon SageMaker

The article announces the general availability of a fully managed MLflow capability on Amazon SageMaker, an open-source tool for managing the machine learning (ML) lifecycle.

Specifically, the article covers:

• Core components of managed MLflow on SageMaker: MLflow Tracking Server, MLflow backend metadata store, and MLflow artifact store
• Benefits of using Amazon SageMaker with MLflow, including comprehensive experiment tracking, efficient server management, enhanced security, and effective monitoring and governance
• Prerequisites for setting up the MLflow Tracking Server environment, including creating a SageMaker Studio domain and configuring the IAM execution role
• Steps to create the MLflow Tracking Server, track and compare training runs, register candidate models, and clean up resources
• Availability and pricing information for the new capability