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Table Of Contents

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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Summary

In this chapter, we compared Go-only and polyglot ML solutions from a practical point of view, contrasting their drawbacks and advantages. We then presented two generic solutions to develop polyglot ML solutions: the os/exec package and JSON-RPC. Finally, we looked at two highly-specialized libraries that come with their own RPC-based integration solutions: TensorFlow and Caffe. You have learned how to decide whether to use a Go-only or polyglot approach to ML in your application, how to implement an RPC-based polyglot ML application, and how to run TensorFlow models from Go.

In the next chapter, we will cover the last step of the ML development life cycle: taking an ML application written in Go to production.

Tech Concepts

Programming languages

Tech Tools