Platform guardrails that keep ML services shippable
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Guardrails are the difference between fast launches and weekend incidents. The best ones are boring, reusable, and enforced by the platform—not socialized via docs.
Guardrails to standardize
- Schema and contract checks: feature availability, ranges, and freshness before training and serving.
- Validation gates: goldens, canary comparisons, and automated rollback triggers wired into CI/CD/CT.
- Runbooks and ownership: clear DRIs, handoff steps, and decision logs for approval or rollback.
How to roll out guardrails
- Ship templates (design docs, dashboards, playbooks) that teams can copy rather than reinvent.
- Add runtime hooks for monitoring and alerts; keep observability defaults in code, not wikis.
- Celebrate rollback drills and postmortems as success metrics, not failures.
Related reading
- Case study: Platform guardrails for ML services.
- Pair with Backtesting ML pipelines before rollout.
- Pillar hub: Practical MLOps.
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