When was the Compression framework introduced?

Train your machine learning and AI models on Apple GPUs

Learn how to train your models on Apple Silicon with Metal for PyTorch, JAX and TensorFlow. Take advantage of new attention operations and quantization support for improved transformer model performance on your devices.

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.

What’s new in Swift

Join us for an update on Swift. We’ll briefly go through a history of Swift over the past decade, and show you how the community has grown through workgroups, expanded the package ecosystem, and increased platform support. We’ll introduce you to a new language mode that achieves data-race safety by default, and a language subset that lets you run Swift on highly constrained systems. We’ll also explore some language updates including noncopyable types, typed throws, and improved C++ interoperability.

Bring your machine learning and AI models to Apple silicon

Learn how to optimize your machine learning and AI models to leverage the power of Apple silicon. Review model conversion workflows to prepare your models for on-device deployment. Understand model compression techniques that are compatible with Apple silicon, and at what stages in your model deployment workflow you can apply them. We’ll also explore the tradeoffs between storage size, latency, power usage and accuracy.