Model Distillation
Transferring knowledge from a large AI model to a smaller, more efficient one.
Definition
Model distillation is a machine learning technique where a smaller "student" model is trained to replicate the behavior of a larger "teacher" model. The goal is to create efficient models that maintain most of the larger model's capabilities while requiring fewer computational resources.
This process involves the student model learning not just from the original training data, but from the teacher model's output probabilities and reasoning patterns. The result is a compact model suitable for deployment on edge devices or cost-sensitive applications.
Why It Matters
Model distillation enables organizations to deploy AI capabilities without the massive infrastructure costs of running large models. This democratizes AI access and makes real-time AI applications practical on mobile devices and embedded systems.
For businesses, distilled models offer a path to cost-effective AI deployment while maintaining quality customer experiences.
Examples in Practice
A mobile app company distills a large language model to run locally on smartphones, enabling offline AI features without cloud costs.
An e-commerce platform uses a distilled recommendation model that processes requests in milliseconds rather than seconds.
A healthcare startup deploys a distilled diagnostic model on medical devices in rural clinics with limited internet connectivity.