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 UI

The crux of any real-time scoring solution is a real-time scoring endpoint, a web URL through which you can pass data and immediately retrieve ML predictions. Endpoints are hosted on containerized services that are up and running 24 hours a day, 7 days a week, waiting for incoming requests.

Requests send data to the endpoint for scoring and can be written in any computer language including Python. As soon as a request comes through, your endpoint will automatically execute the underlying code and return results.

You can use these endpoints anywhere; any coding language from C# to Python to Java can make use of real-time scoring endpoints. Thus, once you obtain the URL that hosts the endpoint, you are free to implement it in any other piece of code. Commonly, real-time scoring endpoints are incorporated in streaming jobs, web applications, and mobile apps.

When using real-time scoring endpoints based on AutoML models, there are...