Apply post-training quantization, pruning, and knowledge distillation to compress models and benchmark accuracy-latency trade-offs.

Configure ONNX Runtime with CUDA and TensorRT execution providers to accelerate inference across hardware targets.

Deploy containerized models to Azure ML online and batch endpoints using autoscaling and blue/green deployment patterns.

Architect and document a complete deep learning engineering lifecycle from distributed training through production deployment.