About this ebook
Ship real AI features to real users, on real devices, under a real battery budget.
Mastering Android AI: From Fundamentals to Production is written for Android developers who know Kotlin and Jetpack Compose but have no machine learning background. It skips the transformer math and the disconnected Python notebooks. Instead it walks through the full decision space Android now offers for intelligent apps, including when to run inference on-device, when to use the cloud, and how to combine both in one feature.
What's inside (25 chapters in 6 parts):
Foundations: why on-device AI matters now, the machine learning vocabulary you need (tokens, embeddings, quantization), and a map of the Android AI ecosystem
On-Device AI Toolchains: project setup, LiteRT, ML Kit, Gemma and open on-device LLMs, custom model deployment at scale, and MediaPipe for real-time perception
Cloud and Generative AI: integrating cloud LLMs, Gemini Nano on the device, building conversational features, and image generation
Feature Deep Dives: computer vision, natural language processing, speech recognition and synthesis, and recommendation systems
Production Readiness: optimizing AI performance, privacy, security and responsible AI, testing AI features, and deploying and monitoring in production
Capstone Projects: an AI-powered camera app, an intelligent personal assistant, and a health and fitness AI companion, plus a look at where the ecosystem is headed
Appendices: a glossary, a model-selection cheat sheet, performance-budget templates and further reading
What makes it different: every code example uses Kotlin and Jetpack Compose. Every architectural decision is weighed against real device fragmentation and a real battery budget. Every feature is built to survive a code review, not just a conference demo. Each chapter ends with a self-check.
Who it's for: Android developers who want to add AI features and need to answer, for any product requirement, "which model, which layer of the stack, on-device or cloud, and why?" No machine learning background is required.
AI models, SDKs and library versions change quickly, so check the current official documentation for version-specific details.
