Model Versioning

Benched.ai Editorial Team

Model versioning assigns unique identifiers to successive checkpoints so changes can be tracked, rolled back, and audited.

Versioning Schemes

Scheme	Format	Example
Semantic	MAJOR.MINOR.PATCH	3.2.1
Date-based	YYYYMMDD	20250615
Git SHA	7-char hash	a1b2c3d
Hybrid	date + short SHA	20250615.a1b2c3d

Workflow

Tag checkpoints immediately after training finishes.
Store metadata (hyperparams, data snapshot ID).
Register in model registry (MLflow, Hugging Face Hub).
Deploy via immutable reference.

Design Trade-offs

Semantic conveys meaning but can cause bikeshedding.
Git SHA guarantees uniqueness but is opaque to humans.
Frequent versions aid rollback yet clutter registries.

Current Trends (2025)

OCI-compliant model artifacts pushed to container registries.
SBOM files attached for supply-chain provenance¹.

Implementation Tips

Enforce version increment via CI checks.
Never overwrite an existing tag; use yanked flag instead.
Include eval metrics in registry for each version.

Linux Foundation, Supply Chain SBOM for AI Models, 2025. ↩