The goal of this book is to show how any developer with basic experience using a command-line terminal and code editor can get started building their own projects running machine learning (ML) on embedded devices. Who Is This Book Aimed At? To build a TinyML project, you will need to know a bit about both machine learning and embedded software development. Neither of these are common skills, and very few people are experts on both, so this book will start with the assumption that you have no background in either of these. The only requirements are that you have some familiarity running commands in the terminal (or Command Prompt on Windows), and are able to load a program source file into an editor, make alterations, and save it.

At QCon SF, Daniel Situnayake presented "Machine learning on mobile and edge devices with TensorFlow Lite". TensorFlow Lite is a production-ready, cross-platform framework for deploying ML on mobile devices and embedded systems, and was the main topic of the presentation. The key takeaways from it included understanding and getting started with TensorFlow Lite, and on-device machine learning on various devices – specifically microcontrollers and optimizing the performance of machine learning models. Situnayake, developer advocate for TensorFlow Lite at Google, began the presentation by explaining what machine learning is. Traditionally a developer feeds rules and data into an application and output answers, while with machine learning the developer or data scientists' feeds in the answers and data and the output are rules, which can be applied in the future.