Run Real AI on Tiny Hardware — No Cloud, No Latency, No Data Leaving the DeviceEvery 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 frameworkShrink models to fit with INT8/INT4 quantization, pruning, and knowledge distillationConvert and deploy models to TensorFlow Lite and TensorFlow Lite Micro on Raspberry Pi, Arduino, and ESP32Build real projects: image classification, person detection, wake-word spotting, and sensor anomaly detectionOptimize for production — balancing power, memory, and speed, with duty-cycling that stretches battery life from hours to monthsShip to mobile with TensorFlow Lite on Android and Core ML on iOS from a single modelRun a complete end-to-end system with over-the-air firmware updates and in-field quality monitoringEvery 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 systemsYou know training, inference, and overfitting — but have never deployed a model to a microcontrollerYou care about privacy, offline reliability, real-time response, and zero per-inference costStop renting intelligence from the cloud. Start building AI that works anywhere — in tunnels, on factory floors, in the field, and off the grid.