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Edge AI & Computer Vision · Free resource

Jetson Edge Inference Deploy Checklist

Ship vision models that survive the field — JetPack, TensorRT, thermal, power, and watchdogs — not just a notebook that scored well once.

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1. Platform baseline

Jetson module + JetPack / L4T version pinned and image flashable offline
Storage partitioned for models, logs, and OS — not a single full rootfs
MAXN / power mode selected for the mission; idle vs. load measured
Camera pipeline (CSI / USB / GStreamer) stable for >1 hour soak

2. Model packaging

Export path clear: PyTorch/ONNX → TensorRT engine for this exact JetPack
FP16 / INT8 choice justified with accuracy regression on holdout set
Input preprocessing identical in training, eval, and onboard code
Engine rebuild script checked in — engines are not mysterious blobs only

3. Runtime & latency

End-to-end latency (capture → detect → act) budgeted and measured
Batch size / resolution trade-off locked for the mission FPS
GPU / DLA utilization logged; thermal throttling observed under sun load
CPU affinity / niceness set so perception does not starve flight bridge

4. Resilience

Process supervisor (systemd / docker restart) recovers from OOM / crash
Graceful degradation: last-known track or safe hover when inference fails
Model + config version reported in telemetry heartbeat
Offline update path (USB / signed package) defined before first demo

Want this applied to your stack? Book a discovery call or request an async audit at edgefield-consulting.pages.dev · aykumar2106@gmail.com

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