Machine Learning with Go Quick Start Guide

By : Michael Bironneau, Toby Coleman

Buy this Book

Machine Learning with Go Quick Start Guide

By: Michael Bironneau, Toby Coleman

Buy this Book

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.

Preface

Who this book is for

What this book covers

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Free Chapter

Introducing Machine Learning with Go

What is ML?

Types of ML algorithms

Why write ML applications in Go?

ML development life cycle

Summary

Further readings

Setting Up the Development Environment

Installing Go

Running Go interactively with gophernotes

Example – the most common phrases in positive and negative reviews

Example – exploring body mass index data with gonum/plot

Example – preprocessing data with Gota

Summary

Further readings

Supervised Learning

Classification

Regression

Summary

Further readings

Unsupervised Learning

Clustering

Principal component analysis

Summary

Further readings

Using Pretrained Models

How to restore a saved GoML model

Deciding when to adopt a polyglot approach

Example – invoking a Python model using os/exec

Example – invoking a Python model using HTTP

Example – deep learning using the TensorFlow API for Go

Summary

Further readings

Deploying Machine Learning Applications

The continuous delivery feedback loop

Deployment models for ML applications

Summary

Further readings

Conclusion - Successful ML Projects

When to use ML

Typical stages in a ML project

When to combine ML with traditional code

Summary

Further readings

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Running Go interactively with gophernotes

Project Jupyter is a not-for-profit organization that was created to develop language-agnostic interactive computing for data science^[3]. The result is a mature, well-supported environment to explore, visualize, and process data that can significantly accelerate development by providing immediate feedback and integrations with plotting libraries such as gonum/plot.

While its first iteration, called iPython, only supported Python-based handlers (called kernels) at first, the latest version of Jupyter has over 50 kernels that support dozens of languages, including three kernels for the Go language^[4]. GitHub has support for rendering Jupyter files (called notebooks)^[5], and there are various specialized hubs for sharing notebooks online, including Google Research Colabs^[6], Jupyter's community hub called NBViewer^[7], and its enterprise...