Knowledge distillation reduces the challenge of deploying model on edge devices with limited resources by transferring knowledge from a large model to a small one without compromising accuracy.
As a result, the small model becomes a compressed, less-expensive version of the large model and can be deployed effectively on less powerful hardware.
To learn more about knowledge distillation, read the blog.
