MLflow
mlops platform
Open-source platform for ML lifecycle
Supported languages
Concepts
runsexperimentsmodelsregistry
Pros and Cons
Ventajas
- + Excellent experiment tracking
- + Model registry
- + Multi-framework support
- + Open-source
- + Complete experiment tracking
- + Integrated model registry
- + ML framework agnostic
- + Intuitive web UI
- + Simplified model deployment
- + Open source with enterprise option (Databricks)
Desventajas
- - UI can be basic
- - Scaling requires work
- - Limited feature store
- - Scalability requires configuration
- - UI can be slow with many experiments
- - Team integration can be complex
- - Some features require Databricks
- - Learning curve for advanced features
Casos de Uso
- Experiment tracking
- Model versioning
- Reproducibility
- Model deployment
- ML experiment tracking
- Model versioning and registry
- Experiment reproducibility
- Model deployment to production
- Model comparison
- ML team collaboration