On-Device AI: ML Foundations for iOS Developers (Paperback) (ISBN-13: 9798183889956)

A practical blueprint for building production iOS apps with on-device AI.

On-Device AI is written for experienced iOS engineers who want to move beyond API calls and understand how to deploy, optimize, and operate machine-learning models locally on Apple silicon.

The book focuses on the craft of shipping models on the device itself. It skips Python-based data science and concentrates on the mobile problems that decide whether an AI feature works in production: thermal behavior, memory use, latency budgets, privacy, model integration, and graceful failure. It treats the model not as a magic black box, but as a fallible computational asset, like a database or a network stack.

Why now: the Apple Neural Engine, unified memory, and Core ML have made local inference practical for real user-facing apps. The opportunity is not just to call AI services, but to build private, responsive intelligence directly into the software people already carry.

You will learn to:

• think in probabilities instead of deterministic if-else logic
• work with the Apple Neural Engine, compiled Core ML models, and the Core ML/Vision stack
• architect model integration, error handling, and graceful fallbacks
• budget latency, memory, and power for production
• know when not to use AI

The missing middle between dense academic ML textbooks and shallow "hello world" tutorials: the systems-engineering view required to ship robust, private, production-grade on-device AI.

Author: Stefan Schmitt
ISBN-13: 9798183889956
Publisher: Independently Published
Language: English
Published: 06/23/2026
Pages: 166
Format: Paperback
Weight: 0.46lbs
Size: 9.00h x 6.00w x 0.42d