<ul><li>Model distillation, also known as knowledge distillation, is a process where a large 'teacher' model transfers its knowledge to a smaller 'student' model.</li><li>The distillation process involves training the student model to mimic the teacher model's behavior by learning from its predictions or internal representations.</li><li>Types of distillation methods include offline distillation, online distillation, and self-distillation, catering to different needs.</li><li>Model distillation has applications in natural language processing, computer vision, and edge computing, offering benefits such as reduced model size, lower computational costs, and faster inference speeds.</li></ul>

Distilling Intelligence: Simplifying Machine Learning Models

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