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

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Understanding how AutoML works on Azure

Before running your first AutoML experiment, it's important to understand how AutoML works on Azure. AutoML is more than just machine learning, after all. It's also about data transformation and manipulation.

As shown in the following diagram, you can divide the stages of AutoML into roughly five parts: Data Guardrails Check, Intelligent Feature Engineering, Iterative Data Transformation, Iterative ML Model Building, and Model Ensembling. Only at the end of this process does AutoML produce a definitive best model:

Figure 2.17 – The Azure AutoML process

Let's take a closer look at each step in this process.

Ensuring data quality with data guardrails

Data guardrails check to make sure that your data is in the correct format for AutoML, and if it is not, it will alter the data accordingly. There are currently six main checks that are performed on your data. Two of the checks – one to...