Model Versioning
Systematic management of different versions of trained ML models including metadata, artifacts, and lineage.
Model versioning tracks all model artifacts, metadata, and lineage – essential for rollbacks and AI governance.
Explanation
Model versioning includes storing model weights, training configuration, data snapshots, and performance metrics for each version. It enables rollbacks, comparisons, and auditing.
Marketing Relevance
Model versioning is a prerequisite for safe rollbacks and regulatory compliance (EU AI Act).
Common Pitfalls
Only versioning weights without training code. Storage costs for many versions. No clear naming convention.
Origin & History
Early ML teams stored models with timestamps in file systems. Git LFS and DVC (2017) brought Git-based versioning. MLflow Model Registry (2018) formalized stage-based versioning. Hugging Face Hub democratized open-source model versioning.
Comparisons & Differences
Model Versioning vs. Data Versioning
Data versioning tracks datasets; model versioning tracks trained models – both are needed for reproducibility.
Model Versioning vs. Code Versioning (Git)
Git versions code (small files); model versioning handles large binary files (GB) with specialized tools.