Use Core ML Tools for machine learning model compression

Discover how to reduce the footprint of machine learning models in your app with Core ML Tools. Learn how to use techniques like palettization, pruning, and quantization to dramatically reduce model size while still achieving great accuracy. Explore comparisons between compression during the training stages and on fully trained models, and learn how compressed models can run even faster when your app takes full advantage of the Apple Neural Engine.

For more on optimizing Core ML, check out “Improve Core ML integration with async prediction" from WWDC23.

Ressources

• Core ML Converters
• Core ML Tools PyTorch Conversion Documentation
• Core ML
• • Vidéo HD
  • Vidéo SD

Vidéos connexes

WWDC20

• Get models on device using Core ML Converters