What is Knowledge distillation? 

‍Knowledge distillation is a technique in machine learning where a smaller model, often referred to as the "student" model, is trained to mimic the behavior of a larger, more complex model, known as the "teacher" model. This process allows the student model to capture the learned representations and predictions of the teacher, effectively transferring the knowledge and performance from the larger model to a more compact, efficient one. It's akin to a student learning from a more experienced mentor, inheriting the mentor's skills and insights, but in a more manageable form. This technique can be particularly useful for deploying machine learning models on devices with limited computational resources, as the distilled model is often faster and requires less memory while maintaining high performance.