Book Image

Machine Learning with Go Quick Start Guide

By : Michael Bironneau, Toby Coleman
Book Image

Machine Learning with Go Quick Start Guide

By: Michael Bironneau, Toby Coleman

Overview of this book

Machine learning is an essential part of today's data-driven world and is extensively used across industries, including financial forecasting, robotics, and web technology. This book will teach you how to efficiently develop machine learning applications in Go. The book starts with an introduction to machine learning and its development process, explaining the types of problems that it aims to solve and the solutions it offers. It then covers setting up a frictionless Go development environment, including running Go interactively with Jupyter notebooks. Finally, common data processing techniques are introduced. The book then teaches the reader about supervised and unsupervised learning techniques through worked examples that include the implementation of evaluation metrics. These worked examples make use of the prominent open-source libraries GoML and Gonum. The book also teaches readers how to load a pre-trained model and use it to make predictions. It then moves on to the operational side of running machine learning applications: deployment, Continuous Integration, and helpful advice for effective logging and monitoring. At the end of the book, readers will learn how to set up a machine learning project for success, formulating realistic success criteria and accurately translating business requirements into technical ones.
Table of Contents (9 chapters)

Example – invoking a Python model using os/exec

To get started with polyglot ML applications, we will revisit the logistic regression example from Chapter 3, Supervised Learning. We will assume that, instead of Go, the model was written in Python and that we wish to invoke it from our Go application. To do this, we will use command-line arguments to pass inputs to the model and read the model's prediction from standard output (STDOUT).

To exchange data between Python and Go, we will use strings formatted using JavaScript Object Notation (JSON). This choice is arbitrary of course[6], and we could have chosen any one of the other formats for which the Go and Python standard libraries have support, such as XML, or invented our own. JSON has the advantage that it takes very little effort to use in both languages.

The process we will follow to communicate with the Python...