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AI-Powered Apps with TensorFlow Lite: Deploy models efficiently on mobile and embedded devices
Paperback
Publisher: Independently Published
Published: Feb 3 2026
Pages: 224
Weight: 0.61
Height: 0.56 Width: 6.00 Depth: 9.00
Language: English
Your users don't have time for latency. They don't want to upload their data. They want answers now.
For years, AI development meant training massive models on massive servers. But the future of AI isn't in a data center-it's in your pocket, on your wrist, and embedded in the devices around you. AI-Powered Apps with TensorFlow Lite is the definitive guide to shrinking your models without shrinking their intelligence.
This book is for mobile developers and embedded engineers who are ready to master On-Device Machine Learning. You will learn how to take heavy, resource-hungry neural networks and transform them into lean, efficient engines that run locally on Android, iOS, and embedded microcontrollers.
Build Faster, Smarter, and Private AppsStop relying on shaky internet connections and expensive API calls. This guide teaches you the full lifecycle of Edge AI deployment.
Model Optimization: Master the art of Quantization and Pruning to reduce model size by 4x without sacrificing accuracy.
Cross-Platform Deployment: Step-by-step tutorials for integrating .tflite models into Android (Kotlin/Java) and iOS (Swift) applications.
Hardware Acceleration: Learn to unlock the full power of the GPU, NPU, and DSP on modern phones for real-time inference.
TinyML & Microcontrollers: Push the boundaries of what's possible by running AI on ultra-low-power devices like the ESP32 and Arduino .
From Python to Production: A clear workflow for training models in TensorFlow and converting them seamlessly for mobile use.
Whether you are building a real-time object detector for a camera app, a smart health monitor, or an offline translation tool, this book gives you the efficiency strategies to make it work.
Make your apps faster, safer, and truly intelligent. Scroll up and grab your copy to master the Edge AI revolution.
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