Model Compression
Model compression is a set of techniques in machine learning and deep learning aimed at reducing the size, computational requirements, and memory footprint of neural network models without significantly sacrificing their performance. It enables the deployment of large models on resource-constrained devices like mobile phones, edge devices, and embedded systems. Common methods include pruning, quantization, knowledge distillation, and low-rank factorization.
Developers should learn model compression when deploying AI models in production environments with limited computational resources, such as mobile apps, IoT devices, or real-time inference systems. It is crucial for reducing latency, lowering power consumption, and minimizing storage costs, making models more efficient and scalable. Use cases include on-device AI for smartphones, autonomous vehicles, and edge computing applications where bandwidth and hardware constraints exist.