--- product_id: 189376612 title: "TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers" price: "£2.48" currency: GBP in_stock: false reviews_count: 13 url: https://www.desertcart.co.uk/products/189376612-tinyml-machine-learning-with-tensorflow-lite-on-arduino-and-ultra store_origin: GB region: United Kingdom --- # TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers **Price:** £2.48 **Availability:** ❌ Out of Stock ## Quick Answers - **What is this?** TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers - **How much does it cost?** £2.48 with free shipping - **Is it available?** Currently out of stock - **Where can I buy it?** [www.desertcart.co.uk](https://www.desertcart.co.uk/products/189376612-tinyml-machine-learning-with-tensorflow-lite-on-arduino-and-ultra) ## Best For - Customers looking for quality international products ## Why This Product - Free international shipping included - Worldwide delivery with tracking - 15-day hassle-free returns ## Description Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures Work with Arduino and ultra-low-power microcontrollers Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML Debug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size 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. ## Features - Highlight, take notes, and search in the book ## Technical Specifications | Specification | Value | |---------------|-------| | Best Sellers Rank | #930,434 in Kindle Store ( See Top 100 in Kindle Store ) #4 in Voice Recognition Software #20 in Computer Image Processing #38 in Pattern Recognition | ## 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. --- ## Why Shop on Desertcart? - 🛒 **Trusted by 1.3+ Million Shoppers** — Serving international shoppers since 2016 - 🌍 **Shop Globally** — Access 737+ million products across 21 categories - 💰 **No Hidden Fees** — All customs, duties, and taxes included in the price - 🔄 **15-Day Free Returns** — Hassle-free returns (30 days for PRO members) - 🔒 **Secure Payments** — Trusted payment options with buyer protection - ⭐ **TrustPilot Rated 4.5/5** — Based on 8,000+ happy customer reviews **Shop now:** [https://www.desertcart.co.uk/products/189376612-tinyml-machine-learning-with-tensorflow-lite-on-arduino-and-ultra](https://www.desertcart.co.uk/products/189376612-tinyml-machine-learning-with-tensorflow-lite-on-arduino-and-ultra) --- *Product available on Desertcart United Kingdom* *Store origin: GB* *Last updated: 2026-05-20*