MLflow is an open-source AI engineering platform designed to help teams manage the complete lifecycle of machine learning and generative AI projects. It provides experiment tracking, model registry, prompt optimization, and observability for both classical ML models and LLM/agent-based applications. MLflow is widely adopted for production-grade MLOps, LLMOps, and supports seamless integration with 100+ AI and ML frameworks, covering tracking, evaluation, prompt registry, deployment, and more.
Visit MLflow's official website for product details and getting started.
Comprehensive guides on how to use MLflow for tracking experiments, managing models, and deploying them.
Access the source code, report issues, and contribute to MLflow on GitHub.