Model Versioning & Tracking
Learn model versioning strategies, experiment tracking, and reproducibility techniques for machine learning projects.
45 min•By Priygop Team•Last updated: Feb 2026
Model Versioning Concepts
- Model Registry: Centralized model storage and management
- Version Control: Track model versions and changes
- Artifact Management: Store model files and metadata
- Reproducibility: Ensure consistent model training
Experiment Tracking
- Hyperparameter Tracking: Log training parameters
- Metrics Recording: Track model performance metrics
- Code Versioning: Link code versions to experiments
- Environment Management: Track dependencies and configurations
MLflow Framework
- MLflow Tracking: Experiment tracking and logging
- MLflow Models: Model packaging and deployment
- MLflow Registry: Model versioning and lifecycle
- MLflow Projects: Reproducible ML workflows
Best Practices
- Consistent naming conventions
- comprehensive metadata logging
- Automated versioning workflows
- Regular model validation and testing