What It Does
MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. It provides experiment tracking, model packaging, a model registry, and deployment tools that work with any ML library or cloud platform.
Key Features
MLflow offers experiment tracking with metrics/params/artifacts, model packaging in a standard format, a centralized model registry with versioning, deployment to multiple targets (Docker, Kubernetes, cloud), LLM evaluation tools, and integration with all major ML frameworks.
When To Use It
Use MLflow when you need reproducible experiments, model versioning, team collaboration on ML projects, or standardized deployment workflows. It excels in organizations with multiple data scientists who need shared experiment visibility.
Alternatives
Weights & Biases provides richer experiment visualization. DVC focuses on data and pipeline versioning. Neptune offers more collaborative features. For Kubernetes-native deployment, Kubeflow provides a more integrated solution.