{"product_id":"mastering-android-ai-app-to-intelligence-9798186929437","title":"Mastering Android AI: App to Intelligence","description":"\u003cp\u003e • Author(s): Sivavishnu R\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Programming - Mobile Devices\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eGo beyond \"add an AI feature\" - understand how on-device and cloud intelligence actually work on Android.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eMost Android AI tutorials give you a checklist: add this dependency, call this API, paste this prompt. This book teaches you the mental model underneath the checklist.\u003c\/p\u003e\u003cp\u003eIf you've ever wondered why NNAPI's deprecation changes how you build fallback logic, what AICore actually checks before deciding a device is eligible for Gemini Nano, how a model can be memory-mapped instead of fully loaded so the OS can evict it under pressure without crashing your feature, or why a model that passes every golden-dataset test can still quietly fail in production - this is the book that answers those questions from the inside out.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eMastering Android AI: From Fundamentals to Production\u003c\/b\u003e takes you from the on-device-vs-cloud trade-off Chapter 1 establishes to the deepest layers of the current Android AI stack: LiteRT delegates and the post-NNAPI hardware path, ML Kit's task APIs, Gemma and other open on-device LLMs, Gemini Nano through AICore, and cloud Gemini through Firebase AI Logic - plus quantization trade-offs, thermal-adaptive performance budgets, and staged production rollouts. You won't just learn which API to call - you'll learn why a given model or delegate is right for one feature and wrong for another, so you can architect features that hold up under a real battery budget, a real device-fragmentation spread, and a real production incident, not just a demo.\u003c\/p\u003e\u003cp\u003eInside, you'll learn: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eHow Android's AI stack fits together - LiteRT delegates and vendor NPU extensions, AICore and Gemini Nano as a platform service, and ML Kit's Play-services-backed updates, and which of the three to reach for on a given feature\u003c\/li\u003e\n\u003cli\u003eThe full lifecycle of an on-device model: quantization trade-offs (FP32 to INT8\/INT4), memory-mapped loading versus full residency, delegate selection across GPU\/NPU\/CPU, and thermal-adaptive patterns that keep a feature usable for ten minutes, not just one inference\u003c\/li\u003e\n\u003cli\u003eHow cloud and on-device generation really work - Gemini's multimodal image generation versus the standalone Imagen API, streaming versus single-shot responses, and the cost-utility math that tells you when a cloud call stops being worth it\u003c\/li\u003e\n\u003cli\u003eHow to architect features that combine both directions - on-device candidate generation with cloud re-ranking, offline-first assistants, and the full privacy spectrum from raw-to-cloud through federated and fully on-device\u003c\/li\u003e\n\u003cli\u003eTesting AI features when there's no single correct answer to assert against - layered golden-dataset, integration, and human-evaluation strategies that catch a silent regression before a user does\u003c\/li\u003e\n\u003cli\u003eThe production lifecycle of a shipped AI feature - canary rollouts, per-user model-version bucketing, drift detection, and rollback, because a model behaving correctly in testing is the start of the relationship, not the end of it\u003c\/li\u003e\n\u003cli\u003ePrivacy and responsible AI as engineering requirements, not policy language - on-device data isolation, subgroup accuracy auditing, and real consent and deletion paths for features that build a behavioral profile\u003c\/li\u003e\n\u003cli\u003eThree shippable capstone projects - an AI-powered camera app, an intelligent personal assistant, and a health-and-fitness AI companion - tying every technique to a feature that has to survive a code review\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eWhether you're an Android developer shipping your first AI feature, a team lead deciding where a feature should sit on the on-device-vs-cloud spectrum, or an engineer inheriting a live AI feature that needs to be made production-safe, this book gives you the mental model professional Android AI engineers use - from the API you call down to the device it runs on.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eNo fluff. No toy demos. Just a rigorous, complete map of how AI features get built and shipped on Android.\u003c\/b\u003e\u003c\/p\u003e","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":48210410930327,"sku":"9798186929437","price":2945.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798186929437.webp?v=1788716280","url":"https:\/\/atlanticbooks.com\/products\/mastering-android-ai-app-to-intelligence-9798186929437","provider":"Atlantic Books","version":"1.0","type":"link"}