On-Device AI: The Complete TinyML Guide for Developers
Deploy, Optimize, and Run Neural Networks on Microcontrollers, Raspberry Pi, and Mobile Hardware — No Cloud RequiredBy Caroline Lennox
Length3h 22m
About this audiobook
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.
Audiobook details
GenreTechnology
Length3 hrs 22 mins
Narrated byListen with 1,000+ voices
FormateBook with Audio
LanguageEnglish
Table of contents
1On-Device AI: The Complete TinyML Guide for Developers
2Chapter 1: Why On-Device AI Changes Everything
3Cloud AI vs Edge AI: The Real Cost
4The TinyML Hardware Landscape
5Latency, Privacy, and Offline Reliability
Show all chaptersShow less
6Who This Book Is For and How to Use It
7Chapter 2: Setting Up Your TinyML Development Environment
8Python Environment and Dependencies
9Raspberry Pi 4 Setup for ML
10Arduino IDE and Board Support Packages
11Edge Impulse CLI Quickstart
12Exercises
13Chapter 3: Neural Network Fundamentals for Constrained Hardware
14Why Standard Deep Learning Models Are Too Big
15Quantization: INT8 and INT4
16Pruning, Knowledge Distillation, and Architecture Search
17MobileNet, SqueezeNet, and MCU-Friendly Architectures
18Chapter 4: Training Your First TinyML Model
19Dataset Preparation and Augmentation
20Building and Training with Keras
21Exporting to TFLite Flatbuffer
22Post-Training Quantization in Practice
23Chapter 5: Deploying Models on Raspberry Pi
24TFLite Python Interpreter: Load, Allocate, Infer
25Real-Time Camera Inference Pipeline
26Coral USB Accelerator and Edge TPU Compilation
27Profiling with perf and TFLite Benchmark Tool
28Chapter 6: Deploying Models on Arduino and ESP32
29TFLite Micro Architecture and Memory Model
30Arduino Nano 33 BLE Sense: IMU Gesture Recognition
31ESP32 Deployment with ESP-IDF and Arduino Core
32Debugging Inference Failures on MCUs
33Chapter 7: On-Device Computer Vision
34Image Classification vs Object Detection at the Edge
35Person Detection with MobileNet V2 SSD
36Camera Capture and Preprocessing Pipeline
37Handling False Positives and Confidence Thresholds
38Chapter 8: On-Device Audio and Keyword Spotting
39PDM Microphone Capture on Arduino
40MFCC Feature Extraction on MCU
41Wake-Word Model: Training to Deployment
42Chapter 9: Anomaly Detection on Sensor Data
43Anomaly Detection Patterns: Autoencoder vs Statistical
44Building and Training the Autoencoder
45Deployment on ESP32 with FreeRTOS
46Chapter 10: Optimizing for Production: Power, Memory, and Speed
47Memory Layout: ROM vs RAM Trade-offs on MCUs
48Model Surgery: Removing Layers, Reducing Filters
49Duty-Cycling and Sleep Modes for Battery Life
50Profiling with ARM Keil and GCC Toolchain
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