Model distillation compresses a large, complex AI model into a smaller, faster one while retaining most of its accuracy. This technique transfers knowledge from a "teacher" to a "student" model, reducing computational costs and latency. It benefits developers deploying AI on edge devices, startups with limited budgets, and enterprises seeking scalable, efficient machine learning solutions without sacrificing performance.
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