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Edge AI in Manufacturing: Computer Vision at Line Speed
Nanostack
Deploy sub-100ms vision models on the factory floor — hardware choices, ONNX optimization, and MLOps for edge devices in 2026.
Cloud inference is too slow for the line — edge wins
Quality inspection at 60+ FPS needs models colocated with cameras. Edge AI cuts latency, keeps sensitive imagery on-prem, and keeps running when WAN links flap.
Architecture that scales across plants
- Model format: ONNX or TensorRT exports with calibrated INT8 where accuracy allows.
- OTA updates: Signed model bundles rolled out per line with automatic rollback on drift alerts.
- Human-in-the-loop: Uncertain predictions queue for operator label — feeding your retrain pipeline.
Results from the field
Teams we work with report 99%+ precision on defect classes after 4–6 weeks of labeled data capture — often replacing manual spot checks. Explore similar builds in our portfolio or start a pilot.