Definition
This category includes methods like pruning, quantization, and knowledge distillation aimed at shrinking model footprint while maintaining performance. It is essential for deploying complex AI models on devices with limited memory, storage, and processing power, enabling faster inference times and lower energy consumption for edge deployment scenarios.
Summary
Model compression refers to techniques that reduce the size and computational requirements of machine learning models.
Key Concepts
- Quantization
- Pruning
- Knowledge Distillation
- Inference Speed
Use Cases
- Deploying models on mobile devices
- Reducing cloud inference costs
- Accelerating real-time video processing
Code Example
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