TL;DR
A machine learning process where a smaller, more efficient consumer-grade model is trained to reproduce the behavior of a larger, highly capable model.
Knowledge distillation transfers complex representations—often called soft probabilities or dark knowledge—learned by a large model to a smaller one, rather than just training it on hard classification labels. This structural compression allows the smaller student model to preserve a high percentage of the original model's accuracy while reducing memory usage and latency. The technique is essential for deploying highly capable AI capabilities on edge systems, hardware-constrained devices, and mobile platforms.
Why this matters for your business
It makes advanced AI capabilities economically viable to run, enabling enterprise-level performance on everyday consumer devices without relying heavily on massive cloud compute resources.
