Edge & Bare Metal AI Ops in the Wild
Lessons from orchestrating GPU fleets across factories and research hubs with zero-trust, OTA updates, and observability.
Edge and bare metal AI deployments are messy—factories lose connectivity, research labs need air gaps, and safety teams demand full traceability. Here is what actually works.
1. Treat Edge as a Product
Each site receives a templated stack: GPU nodes (Jetson/IGX or custom), local message bus, observability sidecar, and OTA agent. Rollouts feel repeatable because they are.
2. Zero-Trust Everything
Device identity, certificate rotation, and policy enforcement ship with the hardware. Telemetry flows through message brokers to a central control plane with anomaly detection.
3. Harden Operations
- OTA updates: Signed releases staged in canary mode before propagating to the fleet.
- Shadow mode: New models run in observe-only mode until confidence thresholds are met.
- Offline playbooks: Local storage buffers data when connectivity drops; sync jobs reconcile later.
4. Measure What Matters
We track uptime, inference latency, incident count, and MTTR per site. Dashboards highlight outliers so operations teams can intervene before downtime hits production.
Edge AI fails when it's treated like "just another deployment." It succeeds when operations get the same respect as model quality.