MLflow

MLflow is an open-source platform for managing the complete machine learning lifecycle, including experimentation, reproducibility, deployment, and a central model registry. MLflow supports both traditional ML and generative AI workflows:

- MLflow Tracking: Record and query experiments, including code, data, config, and results. Now with integrated tracing for GenAI workflows across multiple frameworks.

- MLflow Models: Deploy machine learning models in diverse serving environments, with GenAI support for ChatModels and streaming interfaces.

- Model Registry: Store, annotate, discover, and manage models in a central repository.

- MLflow Evaluation: Evaluate model performance using customizable metrics, including LLM-as-judge frameworks and GenAI-specific benchmarks.

- MLflow Deployments: Simplify model deployment and serving across various platforms, with expanded capabilities for hosting large language models.

Subscribe to our luma calendar for updates about meetups, office hours, and other events: https://lu.ma/mlflow

View code on GitHub here: https://github.com/mlflow/mlflow/

To discuss or get help, please join our mailing list mlflow-users@googlegroups.com