Machine Learning with Go Quick Start Guide: Hands-on techniques for building supervised and unsupervised machine learning workflows

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Setting Up the Development Environment

Just like traditional software development, ML application development requires the mastery of specialist boilerplate code and a development environment that allows the developer to proceed at a pace that has the lowest amount of friction and distraction. Software developers typically waste a lot of time with basic setup and data wrangling tasks. Being a productive and professional ML developer requires the ability to quickly prototype solutions; this means expending as little effort as possible on trivial tasks.

In the previous chapter, we outlined the main ML problems and a development process that you can follow to obtain a commercial solution. We also explained the advantages offered by Go as a programming language when creating ML applications.

In this chapter, we will guide you through the steps that are required to set up a development...

Key benefits

Your handy guide to building machine learning workflows in Go for real-world scenarios

Build predictive models using the popular supervised and unsupervised machine learning techniques

Learn all about deployment strategies and take your ML application from prototype to production ready

Description

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.

What you will learn

Understand the types of problem that machine learning solves, and the various approaches

Import, pre-process, and explore data with Go to make it ready for machine learning algorithms

Visualize data with gonum/plot and Gophernotes

Diagnose common machine learning problems, such as overfitting and underfitting

Implement supervised and unsupervised learning algorithms using Go libraries

Build a simple web service around a model and use it to make predictions

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