On-Device AI: ML Foundations for iOS Developers

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.