MLflow and Model Serving Operations

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Serving rollout playbook

Launch endpoints with latency, quality, and fallback signals.

Serving Rollout Playbook

Model serving turns model quality into a user-facing reliability problem. A rollout plan should combine latency, correctness, traffic, fallback, and ownership.

Readiness checklist

  • Endpoint owner and escalation path.
  • Baseline latency and error budget.
  • Evaluation set and expected quality threshold.
  • Payload logging policy.
  • Rollback plan and fallback behavior.

Release slice

Start with a small internal audience, sample responses, compare against the baseline, and widen traffic only after the signals look stable.

One useful question

If quality drops at 2 p.m., who knows first and what do they do next?

Serving rollout playbook

Serving rollout