Why this matters
In production ML, great models fail without good governance. Governance and approval flows make sure model changes are safe, explainable, compliant, and reversible. Day-to-day tasks you will handle:
- Promoting a model from staging to production with the right sign-offs
- Approving feature store updates that can shift model behavior
- Recording decisions for audits (who approved what, when, and why)
- Rolling back fast when a change causes harm or fails KPIs
- Ensuring privacy, security, and fairness checks happen before deployment
Concept explained simply
Governance = agreed rules + evidence that you followed them. Approval flows = the checkpoints where authorized people review and sign off before a risky change moves forward.
Mental model: Guardrails and gates
Imagine a highway with guardrails (policies) and toll gates (approvals). You can drive fast, but you must pass certain gates:
- Data change gate (new source, schema, or PII risk)
- Model change gate (architecture, objective, or performance shift)
- Deployment gate (release to users)
- Emergency gate (hotfix/rollback)
Each gate asks for standard evidence: tests passed, metrics, bias checks, security review, rollback plan, and sign-offs from defined roles.
Core components of ML governance
- Policies: What must always happen (e.g., 2 approvals for production changes, PII masked, reproducible training).
- Roles and SoD (Segregation of Duties): Requester builds; Reviewer evaluates; Approver signs; Operator deploys. Avoid one person doing all.
- Artifacts: Tickets/PRs, model cards, data sheets, risk assessments, validation reports, and audit logs.
- Environments: Dev → Staging → Prod with clear promotion criteria.
- Gates: Automated checks (tests/metrics) + human approvals at key transitions.
- Auditability: Every decision and artifact is traceable.
- Exception handling: When rules are bypassed (e.g., incident), capture who, why, for how long, and post-incident review.
Minimum viable governance (small teams)
- One mandatory review for data + model changes
- Automated tests: reproducibility, accuracy threshold, canary offline eval
- Model card v1 + rollback plan required for prod
- Single promotion flow: Dev → Staging (auto) → Prod (manual approval)
Worked examples
Example 1: New fraud model to production
- Requester: Submits change with model card, training config, dataset version, metrics vs. baseline, fairness checks, and rollback plan.
- Automated gate: CI runs tests (data drift check, performance, latency, prediction skew on hold-out).
- Reviewers: Data reviewer signs off on data lineage and PII handling; Risk reviewer signs off on fairness and thresholds; Ops reviewer signs off on SLOs.
- Approver: Product/Owner grants production promotion.
- Operator: Deploys to canary, monitors KPIs for 1–24h, then full rollout if stable. All steps logged.
Example 2: Feature store schema change
- Requester: Proposes dropping a feature and adding two engineered features; provides backfill plan and impact analysis.
- Automated gate: Backward-compatibility tests; training reproducibility with new features; monitoring alert simulations.
- Reviewers: Data platform reviewer ensures no downstream breakage; Model owner confirms retraining plan.
- Approver: Tech lead approves timeline and migration steps.
- Operator: Phased rollout; shadow feature logging; rollback enabled.
Example 3: Emergency rollback
- Trigger: Incident—conversion drops 10% after deployment.
- Emergency gate: Bypass normal review with incident ticket and on-call approver (pre-designated).
- Operator: Roll back to last good model within 10 minutes.
- Post-incident governance: Within 48 hours, root-cause analysis, update of tests, and policy adjustments. Approvals and learnings recorded.
Design a basic governance and approval flow (step-by-step)
- List risky events you must gate: data source changes, schema changes, model version bump, hyperparameter overhaul, objective change, deployment to prod.
- Define artifacts per gate: model card, data sheet, validation report, risk/impact assessment, rollback plan.
- Assign roles using RACI: Responsible (build), Accountable (final sign-off), Consulted (domain/privacy/ethics), Informed (stakeholders).
- Set promotion criteria: quantitative thresholds (AUC, latency, fairness deltas), qualitative checks (domain approval).
- Automate checks: tests in CI; generate reports; block merge on failure.
- Manual approvals: at least one independent reviewer + final approver for prod.
- Audit logging: capture who/when/what/artifacts; store immutable records.
- Exception policy: define when and how to bypass (incident only), time limits, and mandatory post-review.
Templates you can copy
Pre-deployment approval checklist
- Reproducible training: seed, config, code, data versions
- Performance vs. baseline: metric deltas and confidence
- Fairness/ethics: segment performance within thresholds
- Security/privacy: PII treatment verified
- Operational: latency/SLOs, autoscaling, alerts configured
- Rollback: validated, tested on staging
- Owner/On-call: documented
- Sign-offs: data, risk, ops, final approver
Change request (CR) minimal fields
- Summary + scope
- Artifact links: PRs, model card, data sheet, validation report
- Risk rating (Low/Med/High) and rationale
- Impact analysis (users, KPIs, dependencies)
- Test evidence (pass/fail)
- Plan: rollout + rollback
- Approvals required and obtained
Risk rating mini-matrix
- Low: minor hyperparameter tweak, no schema change, metrics within ±1%
- Medium: new feature added, retraining, moderate metric shift (±1–5%)
- High: new data source, objective change, sensitive domain, metric shift >5%
Higher risk → more reviewers + longer observation window.
Who this is for
- Machine Learning Engineers and Data Scientists shipping models to users
- MLOps/Platform engineers designing pipelines and gates
- Tech leads who need predictable, auditable releases
Prerequisites
- Basic CI/CD understanding (build, test, deploy)
- Familiarity with model evaluation metrics and data versioning
- Comfort reading PRs/tickets and writing concise documentation
Learning path
- Before: Versioning, testing, and monitoring basics
- Now: Governance and approval flow design, artifacts, roles, and gates
- Next: Automating checks, canary/shadow deployments, and incident response
Common mistakes and how to self-check
- No rollback plan: If rollout fails, can you revert in minutes? If not, fix it now.
- Single-person control: Ensure at least one independent review for prod changes.
- Missing evidence: If you can’t show the model card, data lineage, and validation report, the gate should block.
- Over-governance: If lead time explodes for low-risk changes, tune your risk matrix to right-size approvals.
- Untracked exceptions: Every bypass must be logged with a post-incident follow-up.
Self-check prompt
Pick your last model change. Can you point to:
- The approval(s) with names and timestamps
- The artifacts (model card, validation results, risk rating)
- The exact code/data versions used
- The rollback result in staging
If any are missing, improve your gates.
Exercises — do these now
Do these on paper or in your notes. Keep answers concise and evidence-based.
Exercise 1 (matches Ex1 below): Map a flow for a high-impact model change
Scenario: You’re promoting a new spam classifier that changes the decision threshold and adds a new feature. Define gates, required artifacts, and sign-offs.
Exercise 2 (matches Ex2 below): Build a pre-deployment checklist
Draft a checklist that a reviewer can use in under 5 minutes to approve/reject a prod deployment.
- Checklist: Include reproducibility, metrics, fairness, privacy, ops readiness, rollback, and sign-offs.
- Tip: Mark which items are automated vs. manual.
Practical projects
- Create a model card template and auto-fill it from your training pipeline outputs.
- Implement a “block merge” rule that requires at least one reviewer and all tests green for model PRs.
- Build a risk rating script that labels changes as Low/Med/High from diff metadata and toggles required approvals.
- Set up a simple canary deployment with automatic rollback on KPI regression.
Mini challenge
Design a 1-page governance playbook for your team: risk matrix, required artifacts per risk level, who approves, and the maximum time to rollback. Keep it simple and clear.
Next steps
- Automate artifact generation (model card, validation report) in your CI
- Pilot your approval flow on one model, collect feedback, and iterate
- Run a rollback drill quarterly
Quick Test
Take the quick test to check your understanding. Note: The quick test is available to everyone; only logged-in users have their progress saved.