Your question is Model Optimization and Compression Pipeline. 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 design an efficient model optimization and compression pipeline using quantization, knowledge distillation, and XLA compiler targets for low-latency server-side deployment?
Explain how you would choose between post-training quantization and quantization-aware training, construct the distillation process, and target XLA-compatible hardware. Cover offline optimization, online serving, accuracy-latency-cost evaluation, rollout safety, and monitoring for feature drift and training-serving skew. State assumptions where deployment scale or model details are unspecified.
