🔖 Model Versioning
Track, manage, and deploy ML models with confidence
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Monitoring & Observability
Introduction to Model Versioning
🎯 Why Version Models?
Models evolve continuously through retraining, architecture changes, and hyperparameter tuning. Without proper versioning, you lose reproducibility, can't roll back failures, and struggle to track what's running in production. Version control enables experimentation, comparison, and safe deployments.
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Key Insight
Every model version should be reproducible, trackable, and comparable.
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Reproducibility
Recreate any model version exactly
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Comparison
Compare metrics across versions
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Rollback
Quickly revert to previous versions
📋 What to Version
1
Model Artifacts
Weights, architecture, configuration files
2
Training Code
Scripts, preprocessing pipelines, training logic
3
Data Versions
Training/validation datasets and splits
4
Metadata
Metrics, hyperparameters, environment details
✅ Benefits
- •Track model evolution
- •Enable A/B testing
- •Facilitate collaboration
- •Ensure compliance
⚠️ Without Versioning
- •Lost reproducibility
- •Deployment confusion
- •Difficult debugging
- •Risky rollbacks