MLflow
MLflow is an open-source platform for managing the ML lifecycle, including experimentation, reproducibility, and deployment. It provides tracking, projects, and model registry.
Key Concepts
- Tracking server
- Projects packaging
- Model registry
- Model serving
- Artifact storage
Common Use Cases
- Experiment tracking
- Model versioning
- Reproducible runs
- Model deployment
- Collaboration
Best Practices
- Use consistent experiment naming
- Log all parameters and metrics
- Version datasets with artifacts
- Use model registry for promotion
- Implement proper access control
Resources
- Docs: mlflow.org/docs
- Related Skills: mlops, weights-biases, mllmops