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

Creating real-time endpoints through the SDK

One-click deployment through AML studio is really easy, but most organizations will require you to develop your solutions via code. Luckily, creating real-time scoring endpoints for AutoML models via the AzureML Python SDK is almost as easy as creating them through the UI. Furthermore, you'll gain a deeper understanding of how your endpoints work and how to format your JSON testing to pass data into the endpoint as a request.

In this section, you'll begin by entering your Jupyter environment and creating a new notebook. First, you will deploy your Diabetes-AllData-Regression-AutoML model via ACI, test it, and, once you've confirmed that your test is a success, create a new AKS cluster via code and deploy it there. You will conclude this section by testing your AKS deployment and confirm that everything works as expected.

The goal of this section is to further your understanding of real-time scoring endpoints, teach...