LLM

It seems like you're interested in learning about machine learning and AI models on Apple platforms, particularly in the context of WWDC sessions. Here are some key points from the sessions related to machine learning:

1. Core ML and On-Device Machine Learning:

   • Core ML is a framework that allows you to deploy machine learning models on Apple devices, leveraging the power of Apple Silicon's unified memory, CPU, GPU, and neural engine for efficient and low-latency inference. It automatically segments models across these components to maximize hardware utilization.
   • New features in Core ML include ML tensor types for simplifying model integration, state management for efficient inference, and multifunction models for deploying models with multiple functionalities.

2. Create ML:

   • Create ML is a tool designed to help integrate machine learning into apps across Apple operating systems. It includes features for building image-based models and a new object tracking capability for spatial computing experiences, particularly useful for Apple Vision Pro.

3. Machine Learning on Apple Silicon:

   • Apple Silicon provides a robust platform for machine learning with its efficient compute capabilities. The QLML Tools package helps optimize and convert models for use with Apple frameworks, allowing you to transform PyTorch models into a format optimized for Apple Silicon.

4. Object Tracking for visionOS:

   • The Create ML app now supports object tracking, which is useful for creating immersive experiences on Apple Vision Pro. This involves using photorealistic 3D assets in the USDZ format to train machine learning models for object tracking.

For more detailed exploration, you might want to check out specific sessions like "Explore machine learning on Apple platforms," "Deploy machine learning and AI models on-device with Core ML," and "What’s new in Create ML." Each session provides insights into different aspects of machine learning on Apple platforms.