Knowledge Distillation: A Guide to Distilling Knowledge in a Neural Network

Deploying large machine learning (ML) models in production remains a significant challenge due to their high latency and computational costs during inference, especially for resource-intensive computer vision (CV) models and large language models (LLMs). Knowledge distillation offers a promising solution by enabling knowledge transfer from large, cumbersome models to smaller, more efficient ones. It involves techniques that transfer the knowledge embedded within a large, complex CV model (the "teacher") into a smaller, more computationally efficient model (the "student"). This allows for faster, more cost-effective deployment without significantly sacrificing performance. Practical considerations and trade-offs when applying knowledge distillation in real-world settings are also discussed.

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