You're reading from Automated Machine Learning with Microsoft Azure Build highly accurate and scalable end-to-end AI solutions with Azure AutoML

Product type Paperback

Published in Apr 2021

Publisher Packt

ISBN-13 9781800565319

Length 340 pages

Edition 1st Edition

Languages

Python

Tools

Azure

Concepts

Machine Learning

Authors (2):

Dennis Michael Sawyers

Dennis Sawyers

View More author details

Table of Contents (17) Chapters

Preface

1. Section 1: AutoML Explained – Why, What, and How

2. Chapter 1: Introducing AutoML FREE CHAPTER

3. Chapter 2: Getting Started with Azure Machine Learning Service

4. Chapter 3: Training Your First AutoML Model

5. Section 2: AutoML for Regression, Classification, and Forecasting – A Step-by-Step Guide

6. Chapter 4: Building an AutoML Regression Solution

7. Chapter 5: Building an AutoML Classification Solution

8. Chapter 6: Building an AutoML Forecasting Solution

9. Chapter 7: Using the Many Models Solution Accelerator

10. Section 3: AutoML in Production – Automating Real-Time and Batch Scoring Solutions

11. Chapter 8: Choosing Real-Time versus Batch Scoring

12. Chapter 9: Implementing a Batch Scoring Solution

13. Chapter 10: Creating End-to-End AutoML Solutions

14. Chapter 11: Implementing a Real-Time Scoring Solution

15. Chapter 12: Realizing Business Value with AutoML

16. Other Books You May Enjoy

Chapter 2: Getting Started with Azure Machine Learning Service

Now that we know that the key to delivering return on investment in artificial intelligence is delivering machine learning (ML) projects at a brisk pace, we need to learn how to use Automated Machine Learning (AutoML) to achieve that goal. Before we can do that, however, we need to learn how to use the Azure Machine Learning Service (AMLS). AMLS is Microsoft's premier ML platform on the Azure cloud.

We will begin this chapter by creating an Azure account and creating an AMLS workspace. Once you have created a workspace, you will proceed to create different types of compute to run Python code and ML jobs remotely using a cluster of machines. Next, you will learn how to work with data using the Azure dataset and datastore constructs. Finally, we will provide an overview of AutoML. This will boost your ability to create high-performing models.

In this chapter, we will cover the following topics: