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Machine Learning with Go Quick Start Guide

You're reading from   Machine Learning with Go Quick Start Guide Hands-on techniques for building supervised and unsupervised machine learning workflows

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781838550356
Length 168 pages
Edition 1st Edition
Languages
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Authors (2):
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Michael Bironneau Michael Bironneau
Author Profile Icon Michael Bironneau
Michael Bironneau
Toby Coleman Toby Coleman
Author Profile Icon Toby Coleman
Toby Coleman
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Toc

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:...
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