MLflow: ML Lifecycle Management¶
Quick Facts¶
| Aspect | Details |
|---|---|
| Purpose | End-to-end ML lifecycle |
| Components | Tracking, Models, Registry, Projects |
| Integration | TensorFlow, PyTorch, Scikit-learn |
| Best For | Production model management |
When to Use¶
- Model versioning
- Production deployments
- Model registry
- End-to-end ML workflows
Resources¶
Use when: Managing multiple models in production and need version control.