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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