Knowledge distillation is a technique used to transfer knowledge from large, complex machine learning models, known as "teacher" models, to smaller, more efficient "student" models without significant loss in performance. This process, formalized by Hinton and colleagues, is particularly valuable for deploying models on edge devices with limited computational resources. It involves various strategies such as response-based, feature-based, and relation-based knowledge transfer, and can be implemented through different training schemes like offline, online, and self-distillation. Additionally, knowledge distillation encompasses a range of algorithms, including adversarial, multi-teacher, and cross-modal distillation, each with unique approaches to optimize learning from the teacher model. The technique has found applications across fields like computer vision, natural language processing, and speech recognition, enabling the creation of lightweight models that maintain robust performance. For example, DistilBERT and Amazon Alexa's acoustic modeling use knowledge distillation to achieve smaller, faster models while retaining high accuracy, showcasing its effectiveness in real-world scenarios.