Edge Deployment
Definition pending
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Practical example
Edge deployment runs models on devices at the network edge — phones, IoT sensors, cameras, Raspberry Pis. The model must be small, quantized, and optimized for the target hardware. No cloud dependency, low latency, data stays local. For 90% of edge use cases, a 1–3B parameter quantized model is sufficient.
Workflow example
Edge deployment pipeline: (1) train/acquire model in full precision, (2) quantize (INT8, FP16), (3) convert to edge format: TFLite (Android), CoreML (iOS), ONNX Runtime Mobile, (4) test on target hardware — not on your dev machine!, (5) OTA updates: push model updates to devices over the air, (6) monitoring: track model performance on-device (latency, battery drain, accuracy via user feedback).