Kotlin Multiplatform for AI-Powered Mobile App: Build Android & iOS Applications with Shared Kotlin Code and On-Device Machine Learning Models

Name: Kotlin Multiplatform for AI-Powered Mobile App: Build Android & iOS Applications with Shared Kotlin Code and On-Device Machine Learning Models
Brand: Independently Published
SKU: 9798278173137
Price: 2418.0 INR
Availability: InStock

by Leon Amsel

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ISBN13: 9798278173137
Binding: Paperback
Subject: N/A
Publisher: Independently Published
Publisher Imprint: Independently Published
Publication Date: December 10, 2025
Pages: 338
Original Price: GBP 21.05
Language: English
Edition: N/A
Item Weight: 586 grams
BISAC Subject(s): Programming / General

About The Book

Kotlin Multiplatform for AI-Powered Mobile Apps
Build Android & iOS Applications with Shared Kotlin Code and On-Device Machine Learning Models
Why do so many mobile teams struggle to deliver AI features consistently across Android and iOS? And why does performance often fall short when a model leaves the lab and lands on a real device? These are the barriers every modern developer faces, and they're exactly the challenges this book is built to solve.
Kotlin Multiplatform for AI-Powered Mobile Apps presents a clear, production-focused approach for building intelligent mobile applications that run fast, protect user privacy, and ship reliably on both platforms, all powered by a single shared codebase. Instead of managing two separate implementations, you learn how to align your architecture, preprocessing logic, and ML workflows in one place, while still delivering high-quality native experiences.
This book gives you practical, repeatable strategies grounded in real mobile engineering. You learn how Kotlin Multiplatform, TensorFlow Lite, ONNX Runtime, and modern mobile toolchains work together to support on-device machine learning that users can trust.
Readers will gain the ability to:

Build Android and iOS apps from a unified Kotlin foundation.
Structure shared ML logic that handles preprocessing, inference orchestration, and postprocessing consistently across platforms.
Convert, optimize, and package TensorFlow Lite and ONNX models for mobile deployment.
Benchmark inference performance, reduce latency, and manage memory constraints on real devices.
Integrate ML features into native or shared UI frameworks with clean, testable patterns.
Create model update pipelines, version artifacts correctly, and handle real-world rollout and fallback scenarios.

Whether you're a mobile developer aiming to extend your skill set or an ML practitioner ready to ship intelligent features to millions of users, this book shows you how to bring advanced capabilities to production without complexity or guesswork. You'll learn practical techniques that help you meet performance targets, satisfy app store requirements, and maintain scalable projects that evolve smoothly over time.
If you want to build high-performance cross-platform mobile apps powered by on-device machine learning, and you're ready for a practical guide that translates cutting-edge tools into real, repeatable workflows, secure your copy today and start building the next generation of intelligent mobile experiences.

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