Distillation, in the context of artificial intelligence and machine learning, refers to a technique known as model distillation. This process involves transferring knowledge from a larger, more complex model (often referred to as the “teacher” model) to a smaller, more efficient model (the “student” model). The goal is to create a model that retains much of the performance of the teacher while being less resource-intensive and faster to deploy.

There are several methods of model distillation, including standard knowledge distillation, data-free knowledge distillation, feature-based distillation, and task-specific distillation. Each method has its own focus, such as transferring soft predictions, generating synthetic data, or optimizing for specific tasks like natural language processing or computer vision ²⁴.

Model distillation is particularly useful in scenarios where computational resources are limited, such as mobile applications, real-time systems, and edge computing. By employing this technique, organizations can leverage powerful AI models while ensuring they remain efficient and practical for deployment in various environments ².