Practical on-device AI and ML using Swift

Description

Find out what’s possible in the world of offline, on-device machine learning and artificial intelligence using Swift. Apple’s powerful silicon, combined with the ease of use of Swift, and the associated ML and AI frameworks, including CoreML, CreateML, TensorFlow, and beyond, make it easier than ever to create ML-driven features that exist entirely offline on a user’s iOS device.

In a world of paranoia, lack of privacy and lack of confidence in cloud services and government access, on-device machine learning and artificial intelligence is more important—and thanks to advances low-power silicon, more feasible—than it’s ever been.

Paris Buttfield-Addison and Tim Nugent take a deep dive into what Apple’s Core ML and Vision frameworks do; how to set up your Swift-based iOS development environment for machine learning; how to work with TensorFlow, the popular open source Python neural network and machine learning library, to create, manipulate, and bring models into CoreML and Swift; and how to implement machine learning-based features in your iOS apps and load trained models for use in machine learning.

Join the authors of Practical Artificial Intelligence with Swift (coming late 2019 from O’Reilly) to get up to speed with the latest in machine learning features of iOS, Swift frameworks, and offline, on-device AI. Learn how to apply the Vision and Core ML frameworks to solve practical problems in object detection, face recognition, and more. These frameworks run on-device, so they work quickly with no network access, making them cost effective and user-privacy conscious. You’ll combine Apple’s frameworks with open source libraries Keras and TensorFlow to create an iOS app that makes it look easy to detect faces and facial features, detect and classify objects in photos, and expose these features to the user, and you’ll go beyond this and explore using advanced custom models, and more subtle ML and AI features. You’ll do it all on device. And you’ll move fast.