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)

Deployment models for ML applications

In the preceding example, we explained how to deploy an ML application using Docker to encompass it and its dependencies. We deliberately stayed away from any discussion pertaining to the infrastructure that was going to run these containers or any Platform-as-a-Service offerings that could facilitate the development or deployment itself. In the current section, we consider different deployment models for ML applications under the assumption that the application will be deployed to a cloud platform that supports both IAAS and platform-as-a-service models, such as Microsoft Azure and Amazon Web Services.

This section is specifically written to help you decide what virtual infrastructure to use if you are deploying an ML application to the cloud.

There are two main deployment models for any cloud application:

  • Infrastructure-as-a-service:...