The Problem
Needed real-time object detection with sub-50ms latency on resource-constrained devices.
The Solution
Optimized YOLOv8 with TensorRT, implemented model quantization, and edge deployment.
Architecture
YOLO model, TensorRT optimization, ONNX export, Docker containers on edge devices, Redis queue.
Technology Stack
PythonPyTorchYOLOTensorRTONNXDockerRedis
Outcomes & Impact
40ms avg latency. Successfully deployed to 1000+ edge devices. 98% detection accuracy.
40ms
Latency
98%
Accuracy
1000+
Devices
Lessons Learned
Model optimization and quantization are critical for edge ML. Proper monitoring and A/B testing saved time.