what sessions should I watch to learn more about machine learning
Asked on 06/12/2025
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To learn more about machine learning at Apple's WWDC, you should consider watching the following sessions:
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Discover machine learning & AI frameworks on Apple platforms: This session provides an overview of the machine learning and AI frameworks available on Apple platforms, including the new Foundation Models framework and ML-powered APIs.
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Get started with MLX for Apple silicon: This session introduces MLX, a framework for numerical computing and machine learning on Apple silicon, and covers its key features and how to accelerate MLX.
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Accelerate machine learning with Metal: This session focuses on how to use Metal to accelerate machine learning models, including improvements to MPS and MPS Graph for transformer models.
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Bring your machine learning and AI models to Apple silicon: This session discusses the process of preparing and optimizing machine learning models for deployment on Apple silicon.
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Deploy machine learning and AI models on-device with Core ML: This session covers the integration and execution of machine learning models on-device using Core ML.
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Explore machine learning on Apple platforms: This session provides an overview of machine learning capabilities on Apple platforms, including system-level features and ML-powered APIs.
These sessions will give you a comprehensive understanding of the tools and frameworks available for machine learning on Apple platforms, as well as practical insights into deploying and optimizing models.

Discover machine learning & AI frameworks on Apple platforms
Tour the latest updates to machine learning and AI frameworks available on Apple platforms. Whether you are an app developer ready to tap into Apple Intelligence, an ML engineer optimizing models for on-device deployment, or an AI enthusiast exploring the frontier of what is possible, we’ll offer guidance to help select the right tools for your needs.

Get started with MLX for Apple silicon
MLX is a flexible and efficient array framework for numerical computing and machine learning on Apple silicon. We’ll explore fundamental features including unified memory, lazy computation, and function transformations. We’ll also look at more advanced techniques for building and accelerating machine learning models across Apple’s platforms using Swift and Python APIs.

Accelerate machine learning with Metal
Learn how to accelerate your machine learning transformer models with new features in Metal Performance Shaders Graph. We’ll also cover how to improve your model’s compute bandwidth and quality, and visualize it in the all new MPSGraph viewer.