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Introduction

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Building a Recommendation Engine on Azure
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Overview4m 15s
7
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Overview
DifficultyIntermediate
Duration27m
Students112

Description

Building a Recommendation Engine on Azure is a course designed for teams interested in using artificial intelligence to add product recommendations to their websites.

A product recommendation engine is a valuable feature that helps drive sales on e-commerce sites. In this course, you will learn the essentials of building, deploying, and testing a recommendation engine on Microsoft Azure. You will also build skills to fine-tune a recommendation model and evaluate its effectiveness.

This course is made up of five lectures covering deploying, testing, configuring, evaluating models, and making API requests. This is an intermediate level course, and prior Azure and API experience is recommended.

Learning Objectives

  • Deploy a recommendation engine on Microsoft Azure
  • Test and evaluate different recommendation models
  • Make API calls to the Microsoft Product Recommendations Solution

Intended Audience

  • People who are interested in artificial intelligence services on Microsoft Azure, especially recommendation engines

Prerequisites

  • Experience using Microsoft Azure
  • Experience using APIs

Related Training Content

Resources

The GitHub repository for this course is at https://github.com/cloudacademy/azure-recommendation-engine.

 

Transcript

- [Guy] Welcome to building a recommendation engine on Azure. My name's Guy Hummel. I'm the Azure content lead at Cloud Academy and I have over 10 years of experience with cloud technologies. If you have any questions, feel free to connect with me on LinkedIn and send me a message or send an email to support@cloudacademy.com. 

This course is intended for people who are interested in artificial intelligence services on Azure especially recommendation engines. To get the most from this course, it would be helpful to have some experience using Azure. Ideally, you should also have some experience using APIs, although that's not strictly necessary. 

This course is full of hands-on examples, so I recommend that you try performing these tasks yourself on your own Azure account. If you don't already have one, then you can create a free trial account. To save you the trouble of typing in the commands shown in this course, I've put them in a file in a GitHub repository. You can find a link to the repository at the bottom of the course overview below this video. We'll start with an overview of the recommendation solution we're going to use and how it works. Then I'll show you how to deploy the solution on Azure and test it. After that, we'll go through how to fine-tune the recommendation model.

 Then I'll show you how to evaluate the effectiveness of different models. Finally, you'll see how to make API calls to the recommendation system. By the end of this course, you should be able to deploy a recommendation engine on Azure, test and evaluate different recommendation models and make API calls to the Microsoft Product Recommendation Solution. 

We'd love to get your feedback on this course, so please give it a rating when you're finished. Now if you're ready to learn how to build a recommendation engine on Azure, then let's get started.

About the Author

Students12827
Courses41
Learning paths20

Guy launched his first training website in 1995 and he's been helping people learn IT technologies ever since. He has been a sysadmin, instructor, sales engineer, IT manager, and entrepreneur. In his most recent venture, he founded and led a cloud-based training infrastructure company that provided virtual labs for some of the largest software vendors in the world. Guy’s passion is making complex technology easy to understand. His activities outside of work have included riding an elephant and skydiving (although not at the same time).