---
product_id: 189376612
title: "TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers"
price: "$76.20"
currency: USD
in_stock: false
reviews_count: 13
url: https://www.desertcart.us/products/189376612-tinyml-machine-learning-with-tensorflow-lite-on-arduino-and-ultra
store_origin: US
region: United States of America
---

# TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers

**Price:** $76.20
**Availability:** ❌ Out of Stock

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- **What is this?** TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers
- **How much does it cost?** $76.20 with free shipping
- **Is it available?** Currently out of stock
- **Where can I buy it?** [www.desertcart.us](https://www.desertcart.us/products/189376612-tinyml-machine-learning-with-tensorflow-lite-on-arduino-and-ultra)

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## Description

desertcart.com: TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers eBook : Warden, Pete, Situnayake, Daniel: Kindle Store

Review: Tremendous discussion of running machine learning on resource-limited devices - This is a fantastic, well-written, highly-entertaining resource for devs of all levels curious about running machine learning models on resource-limited devices and looking to play with edge computing. It goes beyond Google's online documentation and gives practical demos and explanations that make sense. Basically, you can follow along the book by running pre-built notebooks in Google Colab to train ML models, then compiling the code to binaries, which you then flashing to the microcontroller - Arduino, SparkFun Edge, and Stm32f7 Discovery Kit are supported with great instructions for all three platforms. For future versions of the book, I'd like to see: - instructions for those working in Windows. All the makefiles and build scripts are MacOS/Linux, but providing a facility for those working in Windows environments would be nice, too (Windows Subsystem for Linux, Visual Studio's nmake, cygwin, cloud environment, virtual machine, etc.). - notebook locations on Github and Google Colab have moved out of 'experimental' status and so the URLs have changed, so some poking around is required to find the code (not hard - the dedicated notebook for the "Hello, world!: example now lives in the /train directory in the repos). - the book doesn't mention having a serial breakout programmer, just the microcontroller and a USB cable. I had to order the serial adapter separately. Overall, the book is really well laid out with a friendly voice and demos that are truly fun to work through. The approach to running the examples, then explaining the concepts for running ML on embedded environments and underlying C++ constructs is a great way to present the material. I prefer this than the traditional 6 chapters of iterative building and only at the end you arrive at the finished product. This gives you something to play with right away.
Review: Recommended - Great book. Helpful well written and helpful to get novice up to speed.

## Technical Specifications

| Specification | Value |
|---------------|-------|
| ASIN  | B082TY3SX7 |
| Accessibility  | Learn more |
| Best Sellers Rank | #361,824 in Kindle Store ( See Top 100 in Kindle Store ) #1 in Voice Recognition Software #10 in Embedded Computer Systems #13 in Pattern Recognition |
| Customer Reviews | 4.4 4.4 out of 5 stars (275) |
| Edition  | 1st |
| Enhanced typesetting  | Enabled |
| File size  | 24.0 MB |
| ISBN-13  | 978-1492052005 |
| Language  | English |
| Page Flip  | Enabled |
| Print length  | 506 pages |
| Publication date  | December 16, 2019 |
| Publisher  | O'Reilly Media |
| Word Wise  | Not Enabled |
| X-Ray  | Not Enabled |

## Images

![TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers - Image 1](https://m.media-amazon.com/images/I/81nihP0ASSL.jpg)

## Customer Reviews

### ⭐⭐⭐⭐⭐ Tremendous discussion of running machine learning on resource-limited devices
*by A***R on May 23, 2020*

This is a fantastic, well-written, highly-entertaining resource for devs of all levels curious about running machine learning models on resource-limited devices and looking to play with edge computing. It goes beyond Google's online documentation and gives practical demos and explanations that make sense. Basically, you can follow along the book by running pre-built notebooks in Google Colab to train ML models, then compiling the code to binaries, which you then flashing to the microcontroller - Arduino, SparkFun Edge, and Stm32f7 Discovery Kit are supported with great instructions for all three platforms. For future versions of the book, I'd like to see: - instructions for those working in Windows. All the makefiles and build scripts are MacOS/Linux, but providing a facility for those working in Windows environments would be nice, too (Windows Subsystem for Linux, Visual Studio's nmake, cygwin, cloud environment, virtual machine, etc.). - notebook locations on Github and Google Colab have moved out of 'experimental' status and so the URLs have changed, so some poking around is required to find the code (not hard - the dedicated notebook for the "Hello, world!: example now lives in the /train directory in the repos). - the book doesn't mention having a serial breakout programmer, just the microcontroller and a USB cable. I had to order the serial adapter separately. Overall, the book is really well laid out with a friendly voice and demos that are truly fun to work through. The approach to running the examples, then explaining the concepts for running ML on embedded environments and underlying C++ constructs is a great way to present the material. I prefer this than the traditional 6 chapters of iterative building and only at the end you arrive at the finished product. This gives you something to play with right away.

### ⭐⭐⭐⭐⭐ Recommended
*by C***S on September 21, 2025*

Great book. Helpful well written and helpful to get novice up to speed.

### ⭐⭐⭐⭐ Good TinyML intro but the SparkFun Edge board used for the examples is no longer available
*by J***O on January 2, 2026*

It's a good intro to the TinyML world! I liked it! Just be aware that the SparkFun Edge board used for the examples is no longer available.

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*Product available on Desertcart United States of America*
*Store origin: US*
*Last updated: 2026-03-18*