Model Compression refers to strategies for reducing the size and complexity of machine learning models while maintaining their performance. Its goal is to make models more efficient in terms of memory utilization, computational requirements, and deployment on limited-resource devices.
Benefits of Model Compression
Reduced computational and storage requirements: Compressed models are smaller, requiring less storage and computational resources for training and deployment, enabling efficient usage of hardware resources.
Faster inference and lower latency: Compressed models often have faster inference times, allowing for quicker predictions and lower latency, which is crucial for real-time applications and services.
Improved scalability and deployment: Compressed models can be easily deployed on resource-constrained devices or edge devices, enabling the scalable and efficient deployment of machine learning models across a variety of platforms and environments.
Xenonstack Glossary
