Platform guardrails for ML services
Context: Multiple teams shipped ranking and ads models independently, causing inconsistent rollout quality, missing metrics, and slow incident response.
Constraints: Strict latency budgets, shared runtime dependencies, and a need to keep delivery velocity while adding safety nets.
Actions and outcomes
- Designed a control-plane contract covering schemas, validation steps, and rollout states (shadow, canary, full).
- Added golden tests, production backtests, and automated rollback triggers tied to business guardrails.
- Shipped dashboards and runbooks that reduced time-to-detect by 45% and enabled same-day rollbacks without on-call escalation.
Artifacts
- Reusable templates for design docs and schema contracts.
- Runbooks and dashboards now used across ads, search, and recommendations services.
- Patterns documented in the Practical MLOps pillar.
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