Model Optimization & Deployment for Deep Learning — Deep Learning & N…
Making a trained model small, fast and servable in production
Steps in Model Optimization & Deployment for Deep Learning
- Model Quantization — advanced · Reducing numerical precision of weights to shrink models and speed up inference
- Model Pruning — advanced · Removing redundant weights or neurons that contribute little to the model's output
- Knowledge Distillation — advanced · Training a smaller 'student' model to mimic a larger 'teacher' model
- Exporting Models (ONNX, TorchScript) — advanced · Converting a trained model into a portable, framework-independent format
- Serving Deep Learning Models — advanced · Wrapping a model in an inference server (TorchServe, Triton, or a custom API)
- GPU/TPU Inference Considerations — advanced · Batching, hardware choice and throughput/latency trade-offs at inference time
- Edge Deployment for Deep Learning — advanced · Running models on mobile devices and other resource-constrained hardware
Part of
- Deep Learning & Neural Networks roadmap — the full learning path