ml

Apple's WWDC sessions cover a wide range of topics related to machine learning (ML) on their platforms. Here are some key highlights from the sessions:

1. Core ML: Core ML is a central framework for deploying machine learning models on Apple devices. It automatically segments models across CPU, GPU, and the neural engine to maximize hardware utilization. New features include ML tensor types for simplifying model integration, multifunction models, and updated performance tools for profiling and debugging models. Explore machine learning on Apple platforms

2. Model Optimization: Apple provides tools to convert and optimize models for Apple hardware, including new model compression techniques and transformer-specific operations. These optimizations help balance storage size, latency, and accuracy. Bring your machine learning and AI models to Apple silicon

3. MLX: MLX is an open-source framework designed for Apple Silicon, offering a unified memory architecture that allows efficient computation across CPU and GPU. It supports advanced ML tasks like inference, training, and quantization, and is compatible with languages like Python and Swift. Get started with MLX for Apple silicon

4. Large Language Models: MLX also supports large language models, providing tools for inference, training, and quantization. This makes it powerful for running language models and ML workflows on Apple hardware. Explore large language models on Apple silicon with MLX

These sessions provide a comprehensive overview of the tools and frameworks available for machine learning on Apple platforms, emphasizing performance optimization and integration with Apple's hardware.