# Governance: Lineage, Model Cards and Access — MLOps

Source: https://www.skillbyai.com/en/mlops/g-gov

> Know what is running, why, and who decided.

## Answerable questions

Good governance means you can answer, for any prediction: which model version made it, trained on which data, with which code, evaluated how, approved by whom. Keep **lineage** (data to run to model version to deployment), **model cards** (purpose, data, metrics including per-group results, limitations, owner), **access control** on data, models and promotion rights, and **audit logs**. Regulated areas (credit, health, employment) may legally require explanations, documentation and human oversight.

## Accountable, adaptable, reviewed

Governance makes models accountable; LLM systems add their own operational needs.

![Three ideas: governance, LLMOps, checklist.](assets/figures/mlops/section-8-map.svg) — Figure 8.1 — Governance, LLMOps and checklist.

## Lineage for one prediction

Each arrow should be queryable.

```text
prediction p-88231 (2026-09-30 14:02)
  <- model cancer-clf v7 (alias champion since 2026-09-12, approved by risk-ml lead)
  <- run 4f2a... (git 9c1e2d, params logged, data fingerprint 04cf6d426dd1)
  <- dataset snapshot 2026-09-01 (validated, 412,331 rows)
  <- evaluation report v7 vs v6 (overall + slices, gate passed)
```

## Restrict who can promote

Separate who can train models from who can move the production alias, and log every promotion.

**Quiz:** What does lineage let you answer?

- [x] Which data, code and approvals produced the model behind a prediction
- [ ] Which colour the dashboard uses
- [ ] How many GPUs exist
- [ ] The CEO's schedule

*Answer:* Which data, code and approvals produced the model behind a prediction. Traceability supports audits and debugging.