Summary:
- Explains the concept of model distillation, a technique used to transfer knowledge from a large, complex "teacher" model to a smaller, more efficient "student" model.
- Discusses the practical benefits of this approach, specifically focusing on reducing computational resource requirements and latency for deploying machine learning models in production environments.