math-modeling-pipeline/phase-5.5-dl

You optimize PyTorch models by building ensembles for robustness, applying knowledge distillation to transfer accuracy to smaller models, and exporting to deployment-ready formats like ONNX. This maximizes accuracy while reducing inference cost.

Model compression for inference is achieved by transferring performance from a large teacher model to a smaller student via knowledge distillation. You then export the student model to ONNX or quantized TorchScript formats to reduce deployment overhead.

Yes, PyTorch ensembles can be exported to ONNX. The workflow supports modular techniques across training and evaluation, producing reproducible pipelines and export-ready artifacts like ONNX or scripted models for benchmarked deployment.

Use model ensembles to improve robustness by averaging multiple model outputs, but use knowledge distillation when you need to reduce inference cost by transferring that ensemble performance into a single, smaller, deployment-ready student model.

Yes, the deep learning optimization workflow includes guardrails for evaluation and benchmarking. It provides end-to-end coverage across training, distillation, and deployment with reproducible pipelines and benchmark reporting.