Amazon SageMaker with MLflow now supports AWS PrivateLink for secure traffic routing

Amazon SageMaker with MLflow now supports AWS PrivateLink, which enables you to transfer critical data from your virtual private cloud (VPC) to MLflow Tracking Servers in a private, secure, and scalable manner. This capability enhances the protection of sensitive information by ensuring that data sent to the MLflow Tracking Servers is transferred within the AWS network, avoiding exposure to the public internet.

MLflow is a popular open-source tool that helps data scientists organize, track, and analyze machine learning (ML) and generative AI (GenAI) experiments. To accelerate ML and GenAI experimentation, you can set-up and manage MLflow Tracking Servers with a couple of clicks in Amazon SageMaker Studio. When you use an AWS PrivateLink, the communication between your VPC and MLflow Tracking Servers is entirely within the AWS network, which provides better security and protects private data. To use Amazon SageMaker with MLflow through your VPC, set up a new VPC Endpoint and connect it to the experiments service. You can create an AWS PrivateLink to connect to MLflow Tracking Servers using the AWS Management Console or AWS Command Line Interface (AWS CLI).

This capability is available in all AWS regions where Amazon SageMaker is currently available, excluding China Regions and GovCloud (US) Regions. To learn more, see connect to an MLflow tracking server through an Interface VPC Endpoint.