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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

Summary

Advanced solutions like the MMSA are at the bleeding edge of ML and AI. It is a truly state-of-the-art technology and now it's another tool in your belt.

You've not only run all three notebooks on the OJ Sales data, but you have also converted the code to take in other datasets and understand how it works. Prepping data, training models, and forecasting the future using the MMSA are all things you have done and could do again. You may already have a use case to which you can apply it, or you may have to wait a few more years until your company is ready, but you are prepared.

Chapter 8, Choosing Real-Time versus Batch Scoring, continues your journey at the forefront of the ML world. Once you build a model in AutoML, the next step is to deploy it, and there are two options: batch versus real-time scoring. You will learn when to use batch scoring, when to use real-time scoring, and the main differences between the two. Mastering these concepts is key to successfully...

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