MLflow 2.18.0

[Yuki Watanabe]

Hi all! We are excited to announce the release of MLflow 2.18.0!

This release includes a number of significant features, enhancements, and bug fixes.

•  Fluent API Thread/Process Safety - MLflow's fluent APIs for tracking and the model registry have been overhauled to add support for both thread and multi-process safety. You are now no longer forced to use the Client APIs for managing experiments, runs, and logging from within multiprocessing and threaded applications.
•  DSPy flavor - MLflow now supports logging, loading, and tracing of DSPy models, broadening the support for advanced GenAI authoring within MLflow. Check out the MLflow DSPy Flavor documentation to get started!
•  Enhanced Trace UI - MLflow Tracing's UI has undergone a significant overhaul to bring usability and quality of life updates to the experience of auditing and investigating the contents of GenAI traces, from enhanced span content rendering using markdown to a standardized span component structure.
•  New Tracing Integrations - MLflow Tracing now supports DSPy, LiteLLM, and Google Gemini, enabling a one-line, fully automated tracing experience. These integrations unlock enhanced observability across a broader range of industry tools. Stay tuned for upcoming integrations and updates!
•  Expanded LLM-as-a-Judge Support - MLflow now enhances its evaluation capabilities with support for additional providers, including Anthropic, Bedrock, Mistral, and TogetherAI, alongside existing providers like OpenAI. Users can now also configure proxy endpoints or self-hosted LLMs that follow the provider API specs by using the new proxy_url and extra_headers options. Visit the LLM-as-a-Judge documentation for more details!
•  Environment Variable Detection - As a helpful reminder for when you are deploying models, MLflow now detects and reminds users of environment variables set during model logging, ensuring they are configured for deployment. In addition to this, the mlflow.models.predict utility has also been updated to include these variables in serving simulations, improving pre-deployment validation.

 Breaking Changes to ChatModel Interface
As part of a broader unification effort within MLflow and services that rely on or deeply integrate with MLflow's GenAI features, we are working on a phased approach to making a consistent and standard interface for custom GenAI application development and usage. In the first phase (planned for release in the next few releases of MLflow), we are marking several interfaces as deprecated, as they will be changing. Please visit the MLflow website for the detailed changes.

There are many more enhancements and bug fixes in this release! For the comprehensive list of changes, please visit the release post on our website!