For organisations that cannot deploy a model registry tool, model management reverts to manual file management with a tracking spreadsheet. A dedicated directory or cloud storage bucket is organised by model name and version (for example, models/recruitment_screener/v2.3/), with access controls preventing unauthorised modification.

The model tracking spreadsheet serves as the registry’s metadata layer. It should include columns for model name, version, storage location, content hash (SHA-256), training data version, code commit, training date, evaluation metrics (performance and fairness), stage (experimental, staging, production, or archived), approval evidence (approver name and date), and deployment date. Stage transitions from staging to production require a signed approval entry by the AI Governance Lead.

No model artefact may be deleted; archived models are moved to a separate archive directory. The Technical SME follows the provenance chain manually: look up the model version in the spreadsheet, find the code commit, find the data version. This approach is manageable for a single system with infrequent model updates but becomes burdensome for multiple systems or frequent retraining. MLflow is open-source and free; the cost is engineering time for integration.

Key outputs

• Directory structure with access controls
• Model tracking spreadsheet with all required metadata columns
• Signed approval entries for production stage transitions
• No-delete policy for archived model artefacts