Deploying Computer Vision to the Edge
Jetson, OpenVINO, Coral and the model-optimisation tricks that make production CV run offline at the plant.
Why the edge, not the cloud
Manufacturing lines can't tolerate cloud latency or WAN outages. Retail cameras generate more video than any WAN could ship. Medical imaging can't leave the hospital. Edge deployment is not a preference — it's a requirement.
Pick hardware by workload
NVIDIA Jetson Orin for heavy models (segmentation, tracking, multi-camera). Intel OpenVINO on existing industrial PCs for detection and classification. Coral TPUs for ultra-low-power always-on cameras. We benchmark each candidate on your actual model + throughput before committing.
Optimisation tricks that matter
INT8 quantisation with per-channel calibration recovers 90–98% of FP32 accuracy at 2–4x speedup. TensorRT layer fusion knocks another 30–50% off. For very tight budgets, distillation from a big teacher to a small student wins where quantisation alone can't. We track accuracy vs. latency Pareto — never ship a smaller model without measuring the accuracy tax.
MLOps at the edge
Model registry + OTA updates + rollback are mandatory. Every device reports inference metrics and edge-case captures back to a central store. Retraining cadence closes the loop: production edge cases feed the next training run.
The economics
For 100+ camera deployments, edge inference costs 5–20x less than cloud, even after amortising hardware. And latency is deterministic — 30ms per frame, every frame, forever.
Key takeaways
- Edge deployment beats cloud on latency, cost and reliability at scale
- Jetson for heavy, OpenVINO for x86, Coral for always-on low-power
- INT8 quantisation + TensorRT typically wins 3–5x speedup with minimal accuracy loss
- OTA updates, model registry and rollback are non-negotiable
- Production edge cases feed retraining — the loop matters more than the model
T7 Research
Enterprise AI Research Group
T7 Research is the research arm of T7 Solution, focused on benchmarking LLMs, evaluating RAG patterns, and compiling implementation playbooks for enterprise technology leaders.