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 12: Realizing Business Value with AutoML

You have acquired a wide variety of technical skills throughout this book. You're now able to train regression, classification, and forecasting models with AutoML. You can code AutoML solutions in Python using Jupyter notebooks, you know how to navigate Azure Machine Learning Studio, and you can even integrate machine learning pipelines in Azure Data Factory (ADF). Yet, technical skills alone will not guarantee the success of your projects. In order to realize business value, you have to gain the trust and acceptance of your end users.

In this chapter, you will begin by learning how to present end-to-end architectures in a way that makes it easy for end users to understand. Then, you will learn which visualizations and metrics to use to show off your model's performance, after which you will learn how to visualize and interpret AutoML's built-in explainability function.

You will also explore options to run...