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Automated Machine Learning with Microsoft Azure

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

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Product type Paperback
Published in Apr 2021
Publisher Packt
ISBN-13 9781800565319
Length 340 pages
Edition 1st Edition
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Authors (2):
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Dennis Michael Sawyers Dennis Michael Sawyers
Author Profile Icon Dennis Michael Sawyers
Dennis Michael Sawyers
Dennis Sawyers Dennis Sawyers
Author Profile Icon Dennis Sawyers
Dennis Sawyers
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Toc

Table of Contents (17) Chapters Close

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

Architecting real-time scoring solutions

Real-time inferencing refers to scoring new data points as they arrive instead of on a time-based schedule. New data flows in, new predictions come out. While not as common as batch inferencing, real-time inferencing is used by companies in a number of scenarios such as credit card fraud detection, anomaly detection on the factory floor, and recommending products when you're online shopping.

In this section, you will learn how to architect a complete, end-to-end real-time scoring solution using Azure AutoML-trained models. You will also learn why, and in what situations, you should prioritize real-time scoring over batch scoring solutions.

Understanding the four-step real-time scoring process

Real-time scoring solutions follow a slightly different process than batch scoring solutions. There are only four steps. Like batch solutions, the process begins by training an ML model and registering it as you did in previous chapters....

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