Practical MLOps
From reproducible pipelines to CI/CD/CT, this pillar shares practical guidance on MLOps and platform building blocks that keep shipping fast while staying reliable.
Recommended reading order
- Read the platform overview posts.
- Move to deployment automation and validation steps.
- Finish with observability and incident response.
Start here
- Backtesting ML pipelines before rollout — Golden runs, replay tests, and failure injection that catch regressions before canary.
- Platform guardrails that keep ML services shippable — Contracts, validation gates, and rollback drills that make model delivery predictable across teams.
Posts in this pillar
- Backtesting ML pipelines before rollout — Golden runs, replay tests, and failure injection that catch regressions before canary.
- Platform guardrails that keep ML services shippable — Contracts, validation gates, and rollback drills that make model delivery predictable across teams.
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