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Deploy machine learning and AI models on-device with Core ML
Learn new ways to optimize speed and memory performance when you convert and run machine learning and AI models through Core ML. We'll cover new options for model representations, performance insights, execution, and model stitching which can be used together to create compelling and private on-device experiences.
Chapters
- 0:00 - Introduction
- 1:07 - Integration
- 3:29 - MLTensor
- 8:30 - Models with state
- 12:33 - Multifunction models
- 15:27 - Performance tools
Resources
Related Videos
WWDC23
- Improve Core ML integration with async prediction
- Use Core ML Tools for machine learning model compression
Tech Talks
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