juniorMLOps
What is model versioning in MLOps?
Updated May 17, 2026
Short answer
Model versioning tracks different iterations of ML models for reproducibility and rollback.
Deep explanation
It ensures every trained model is stored with metadata like dataset version, hyperparameters, and metrics. Tools like MLflow help manage versions systematically.
Real-world example
Rolling back a deployed fraud model after performance degradation.
Common mistakes
- Not linking models to dataset versions.
Follow-up questions
- Why is versioning critical?
- What tools support versioning?