Model distillation in AI is a technique for creating smaller and more efficient
AI models by
learning from larger models, preserving their knowledge while reducing complexity and computational requirements.
In the distillation process, a large, complex model (called the teacher model) transfers knowledge to a smaller, lighter model (student model). This process goes beyond simply copying the final results: the small model
learns to think similarly to the large model, capturing its
reasoning approach and decision
patterns.
It's like having an expert teacher (large model) who teaches all their knowledge to a student (small model), allowing them to capture the essence of
learning without needing to memorize every detail. The result is a more compact model that can run on devices with fewer computational resources while maintaining performance close to the original model.
This technique is crucial for implementing AI on devices with limited capabilities such as mobile phones, embedded systems, or wearables, allowing complex models to operate in environments with memory and processing constraints. Currently, most complex
AI models have distilled versions that facilitate their implementation in various contexts and devices, maintaining an optimal balance between performance and efficiency.