Introducing Machine Learning with Go

All around us, automation is changing our lives in subtle increments that live on the bleeding edge of mathematics and computer science. What do a Nest thermostat, Netflix's movie recommendations and Google's Images search algorithm all have in common? Created by some of the brightest minds in todays software industry, these technologies all rely on machine learning (ML) techniques.

In February 2019, Crunchbase listed over 4,700 companies that categorized themselves as Artificial Intelligence (AI) or ML[1]. Most of these companies were very early stage and funded by angel investors or early round funding from venture capitalists. Yet articles in 2017 and 2018 by Crunchbase, and the UK Financial Times, center around a common recognition that ML is increasingly relied upon for sustained growth[2], and that its increasing maturity will lead to even more widespread applications[3], particularly if challenges around the opacity of decisions made by ML algorithms can be solved[4]. The New York Times even has a column dedicated to ML[5], a tribute to its importance in everyday life.

This book will teach a software engineer with intermediate knowledge of the Go programming language how to write and produce an ML application from concept to deployment, and beyond. We will first categorize problems suitable for ML techniques and the life cycle of ML applications. Then, we will explain how to set up a development environment specifically suited for data science with the Go language. Then, we will provide a practical guide to the main ML algorithms, their implementations, and their pitfalls. We will also provide some guidance on using ML models produced using other programming languages and integrating them in Go applications. Finally, we will consider different deployment models and the elusive intersection between DevOps and data science. We will conclude with some remarks on managing ML projects from our own experience.

In our first chapter, we will introduce some fundamental concepts of Go ML applications:

• What is ML?
• Types of ML problems
• Why write ML applications in Go?
• The ML development life cycle