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Apple is dedicated to advancing state-of-the-art machine learning technologies. Deep integration between our hardware, software and tools provides a unique ML ecosystem in which it is easy for researchers and developers to infuse intelligence into our products. On-device machine learning capabilities (CoreML, CreateML), combined with hardware acceleration make model training and inference fast and efficient. Apple enables training of models at scale on machine learning platforms and tools to further improve the ML lifecycle, by simplifying the process of training models at scale on cloud compute infrastructure, deploying these models to devices reliably, and evaluating their performance. Another fundamental principle of Apple’s dedication to advancing the start-of-the-art in machine learning, is to do so responsibly, in a manner which protects the privacy of our users. We achieve this by combining the opportunities afforded through on-device ML with our powerful server-side platform and tools, to enable federated learning with differential privacy at scale.

In this talk, we will provide more details of Apple’s unique ML ecosystem, showcasing how easy it is to do use, and how we do so in a way which protects the privacy of our users.

Presenter: Gaurav Kapoor.

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