Kotlin Multiplatform for AI-Powered Mobile App: Build Android & iOS Applications with Shared Kotlin Code and On-Device Machine Learning Models
Language: English
Published by Independently published, 2025
- Softcover
- New
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
AbeBooks seller since January 28, 2020
Condition: New
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- Title
- Kotlin Multiplatform for AI-Powered Mobile App: Build Android & iOS Applications with Shared Kotlin Code and On-Device Machine Learning Models
- Author
- Amsel, Leon
- Publisher
- Independently published
- Publication year
- 2025
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 13
- 9798278173137
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.
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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GreatBookPricesUK
Woodford Green, United Kingdom
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