Model distillation trains a smaller, faster "student" model to mimic a larger, complex "teacher" model. This compresses AI while retaining accuracy. It is used to deploy efficient models on edge devices, mobile apps, or real-time systems. Developers, data scientists, and companies with limited hardware benefit from reduced costs, latency, and energy consumption.
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