On-Device AI: The Complete TinyML Guide for Developers

Deploy, Optimize, and Run Neural Networks on Microcontrollers, Raspberry Pi, and Mobile Hardware — No Cloud Required

Caroline Lennox

• Publisher: Chiify
• eBook ISBN:
  9798259609723
• Year: 2026

Run Real AI on Tiny Hardware — No Cloud, No Latency, No Data Leaving the Device Every cloud inference call costs money, leaks data, and stalls the moment the network drops. What if your models ran directly on a $10 microcontroller instead? On-Device AI: The Complete TinyML Guide for Developers is a hands-on, code-first roadmap to deploying neural networks on microcontrollers, Raspberry Pi, and mobile hardware. Written for developers who understand the basics of machine learning but have never squeezed a model into kilobytes of RAM, it takes you from core concepts all the way to shipping a production system — one you can build and run yourself. You won't just read about TinyML. You'll train models, quantize them to a fraction of their size, deploy them to real boards, and debug the failures that only happen on constrained hardware. Inside the book, you'll learn how to: Choose the right hardware for your workload — MCUs, DSPs, NPUs, and FPGAs — using a clear decision framework Shrink models to fit with INT8/INT4 quantization, pruning, and knowledge distillation Convert and deploy models to TensorFlow Lite and TensorFlow Lite Micro on Raspberry Pi, Arduino, and ESP32 Build real projects: image classification, person detection, wake-word spotting, and sensor anomaly detection Optimize for production — balancing power, memory, and speed, with duty-cycling that stretches battery life from hours to months Ship to mobile with TensorFlow Lite on Android and Core ML on iOS from a single model Run a complete end-to-end system with over-the-air firmware updates and in-field quality monitoring Every chapter is code-first, followed by clear explanations and exercises designed to break your assumptions and force you to debug real problems — the way you actually learn. This book is for you if: You write C, C++, or Python and have shipped (or want to ship) software to embedded systems You know training, inference, and overfitting — but have never deployed a model to a microcontroller You care about privacy, offline reliability, real-time response, and zero per-inference cost Stop renting intelligence from the cloud. Start building AI that works anywhere — in tunnels, on factory floors, in the field, and off the grid.

Table of contents

• On-Device AI: The Complete TinyML Guide for Developers
• Copyright
• Chapter 1: Why On-Device AI Changes Everything
• Chapter 2: Setting Up Your TinyML Development Environment
• Chapter 3: Neural Network Fundamentals for Constrained Hardware
• Chapter 4: Training Your First TinyML Model
• Chapter 5: Deploying Models on Raspberry Pi
• Chapter 6: Deploying Models on Arduino and ESP32
• Chapter 7: On-Device Computer Vision
• Chapter 8: On-Device Audio and Keyword Spotting
• Chapter 9: Anomaly Detection on Sensor Data
• Chapter 10: Optimizing for Production: Power, Memory, and Speed
• Chapter 11: On-Device AI on Mobile: Android and iOS
• Chapter 12: Building a Production TinyML System End-to-End