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Advanced project·3-4 hours

ML Serving Readiness Project

Turn quality evidence and traffic assumptions into a capacity model, SLO, rollout, and rollback plan.

Scenario

A model endpoint must serve 120 requests per second at peak while meeting a p95 latency target. One warm replica sustains 35 requests per second at the target latency.

You will demonstrate

  • Compute capacity with headroom.
  • Define complete serving SLOs.
  • Design canary and rollback evidence.

Project evidence

Complete each deliverable.

Runnable Python lab

Calculate replica capacity with headroom

Compute the minimum whole number of replicas for peak traffic and a utilization headroom policy.

Project defense

Why is average latency insufficient for an online inference SLO?

Assessment rubric

  • Capacity includes explicit headroom.
  • Tail latency and queueing are measured.
  • Quality remains a release dimension.
  • Rollback is automatic for defined critical failures.

Project completion gate

Complete all deliverables, pass the automated code checks, and defend the key decision.

Deliverables 0/4Code pendingDefense pending