Deployment Maturity Model

Maturity stages

STAGE 1 - LAB WIN
  single benchmark uplift under controlled setup

STAGE 2 - REPRODUCIBLE PACKAGE
  fixed metadata, repeatable scripts, and owner assignment

STAGE 3 - PRODUCTION SHADOW
  realistic traffic replay with SLA and quality checks

STAGE 4 - GUARDED ROLLOUT
  bounded rollout plan, guardrails, and rollback path

STAGE 5 - SUSTAINED OPERATION
  long-window monitoring with incident replay coverage

• Do not promote changes between stages without explicit evidence.

• Ownership and rollback plans are mandatory before guarded rollout.

• Post-rollout telemetry must map back to the original hypothesis.

AI accelerator deep dive

Accelerator outcomes are cross-layer effects of mapping, memory behavior, and runtime policy.

Concept diagram

workload -> mapping -> memory and compute behavior -> SLA outcome

Metric graph

throughput / latency / perf-per-watt trend

Metrics and artifacts to collect

• throughput and latency profile

• power and thermal telemetry

• root-cause artifact packet

Mini case study

Freeze revisions and isolate first failing workload slice before optimization debate.

Debug branches

• Classify bottleneck

• Collect reproducible evidence

• Apply bounded fix

Senior review question

Ask: which first-principles bottleneck class explains the symptom, and what artifact proves it reproducibly?