math-modeling-pipeline/phase-5.5-dl

What problem does it solve?

Phase 5.5-DL tackles the challenge of squeezing maximum performance from deep learning models by combining ensemble methods, knowledge distillation, inference-time optimizations, and model compression to achieve higher accuracy and more efficient deployment.

Core Features & Use Cases

• Ensemble modeling to improve robustness and performance by averaging outputs from multiple models.
• Knowledge distillation to transfer the performance of a strong teacher model to a smaller student, reducing inference cost.
• Inference optimization and model compression through export to ONNX or TorchScript/quantized formats for deployment.
• End-to-end DL optimization coverage across training, evaluation, and deployment with reproducible pipelines and benchmark reporting.

Quick Start

Provide an end-to-end optimization workflow by building an ensemble, applying distillation, and exporting optimized models for deployment.