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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.

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.

What’s new in Create ML

Explore updates to Create ML, including interactive data source previews and a new template for building object tracking models for visionOS apps. We’ll also cover important framework improvements, including new time-series forecasting and classification APIs.

Discover Swift enhancements in the Vision framework

The Vision Framework API has been redesigned to leverage modern Swift features like concurrency, making it easier and faster to integrate a wide array of Vision algorithms into your app. We’ll tour the updated API and share sample code, along with best practices, to help you get the benefits of this framework with less coding effort. We’ll also demonstrate two new features: image aesthetics and holistic body pose.

Platforms State of the Union

Discover the newest advancements on Apple platforms.

Support real-time ML inference on the CPU

Discover how you can use BNNSGraph to accelerate the execution of your machine learning model on the CPU. We will show you how to use BNNSGraph to compile and execute a machine learning model on the CPU and share how it provides real-time guarantees such as no runtime memory allocation and single-threaded running for audio or signal processing models.