Edge AI deployment playbook

Edge rollouts mix hardware constraints with software velocity. Here’s how we keep inference fast, updates safe, and operators in control.

Start with constraints, not models

• Define power, memory, and connectivity budgets before choosing an architecture.
• Lock target latency (p50/p95) per use case, not per model.
• Decide offline vs. online update cadence based on safety requirements.

Packaging and transport

• Containerize with stripped runtimes; avoid glibc bloat and unused drivers.
• Sign artifacts and verify on-device before activation.
• Use staged rings: lab → pilot sites → broad release.

Observability on the edge

• Ship lightweight traces: input shape stats, cold-start counts, and per-layer timing.
• Cache health beacons locally when connectivity drops; flush on reconnect.
• Alert on drift of embedding norms or class distributions, not just errors.

Safe rollbacks

• Keep N-1 artifacts on device with pre-warmed caches.
• Flag flips happen server-side; device enforces guardrails if control plane is unreachable.
• Rehearse brownouts: simulate packet loss and firmware rollbacks quarterly.

Govern the loop