Model Compression
Techniques to reduce model size and computational requirements (quantization, pruning, distillation) for efficient deployment.
Related Concepts
- Quantization: Explore how Quantization relates to Model Compression
- Pruning: Explore how Pruning relates to Model Compression
- Knowledge Distillation: Explore how Knowledge Distillation relates to Model Compression
Why It Matters
Understanding Model Compression is crucial for anyone working with ai infrastructure & deployment. This concept helps build a foundation for more advanced topics in AI and machine learning.
Learn More
This term is part of the comprehensive AI/ML glossary. Explore related terms to deepen your understanding of this interconnected field.
Tags
Related Terms
Knowledge Distillation
Training a smaller 'student' model to mimic a larger 'teacher' model, transferring knowledge while reducing size.
Pruning
Removing unnecessary weights or neurons from a trained model to reduce size and computation while maintaining performance.
Quantization
Reducing model precision (FP32 → INT8) to decrease size and increase inference speed with minimal accuracy loss.