MLflow is an open-source tool for managing the full lifecycle of machine learning models and GenAI applications. It helps teams log experiments, version models, and monitor behavior from training through production.
GenAI observability and control
MLflow provides end-to-end observability for generative apps and AI agents, so developers can review what happened and compare changes over time.
Track prompts and responses
Monitor quality metrics and evaluation results
Compare runs and versions across iterations
Use the built-in AI gateway and tracking to work with multiple model providers, including OpenAI, Anthropic, Gemini, and others
Training, registry, and deployment workflow
MLflow structures the training-to-production path by centralizing experiment tracking and model management.
Log hyperparameters, metrics, and artifacts
Register and version models
Support common frameworks such as PyTorch, TensorFlow, Keras, HuggingFace, and Spark
Fits into your existing data stack
MLflow integrates into existing infrastructure, connecting to data pipelines and orchestration systems.
Works with 40+ applications and frameworks
Suitable for research teams and large production environments
