what new in ml processing

At WWDC, Apple introduced several new advancements in machine learning processing across its platforms. Here are some key highlights:

1. Core ML Enhancements: Core ML continues to be the primary framework for deploying machine learning models on Apple devices. It now includes new features such as ML tensor types for simplifying model integration, multifunction models for deploying models with multiple functionalities, and updated performance tools for profiling and debugging models. These enhancements help in efficiently deploying and running machine learning and AI models on-device (Deploy machine learning and AI models on-device with Core ML).

2. BNNS Graph API: The BNNS Graph API has been updated with a new graph builder, allowing developers to create graphs of operations. This enables writing pre and post-processing routines or small machine learning models to run in real-time on the CPU (Discover machine learning & AI frameworks on Apple platforms).

3. Metal 4 Integration: Metal 4 introduces new features for combining machine learning workloads with graphics. The ML command encoder allows entire machine learning networks to run directly on the GPU timeline, enabling seamless integration with compute and render work. ShaderML and the Metal Performance Primitives Library allow embedding machine learning operations directly into shaders (Combine Metal 4 machine learning and graphics).

4. MLX for Apple Silicon: MLX is a new tool designed to take advantage of Apple Silicon's unified memory architecture. It allows for efficient operations across CPU and GPU without the need for memory copying, making it easier to run large models and experiment with unique architectures (Get started with MLX for Apple silicon).

5. CreateML Updates: The CreateML app now includes an object tracking template and new time series classification and forecasting components, making it easier to train and deploy models on Apple platforms (Explore machine learning on Apple platforms).

These updates reflect Apple's ongoing commitment to enhancing machine learning capabilities across its ecosystem, providing developers with powerful tools to create innovative applications.