Why We Deploy Computer Vision at the Edge
Sending camera feeds to the cloud is often the wrong default for industrial inspection. Edge inference solves latency, cost, and privacy at once.
When a camera has to decide whether a product passing on a line is defective, the decision has to happen in the time it takes the product to move past. That constraint shapes the entire architecture, and it usually rules out sending the feed to a distant server.
Latency is the deciding factor
Round-tripping video to the cloud introduces delay that a fast production line cannot absorb. Running the model on hardware next to the camera — at the edge — keeps inference within the required window and lets the system act on the result immediately.
Cost and privacy come along for free
Edge inference also avoids the bandwidth and storage cost of streaming continuous high-resolution video, and it keeps footage inside the customer's network. For many industrial clients, that last point is not a preference — it is a hard requirement.
The engineering trade-off
The cost of edge deployment is that models must be optimized to run on constrained hardware. Quantization and runtimes like TensorRT and ONNX let us fit accurate models onto devices like the Jetson family without giving up the detection quality the application needs.