Your question is Optimize Inference for Edge Deployment. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
How would you optimize a deep learning model's inference latency using quantization or pruning techniques for deployment on edge hardware at Torc Robotics?
Explain how you would establish a representative latency baseline, select between post-training and quantization-aware training, apply structured or unstructured pruning, and validate accuracy after each optimization. Describe the deployment path, hardware-specific profiling, and safeguards against accuracy, numerical, and operational regressions. Provide production-quality Python code that demonstrates the optimization and benchmarking workflow.