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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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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

Chapter 7: Using the Many Models Solution Accelerator

Now that you have experienced building regression, classification, and forecasting models with AutoML, it's time for you to learn how to deploy and utilize those models in actual business scenarios. Before you tackle this, however, we will first introduce you to a new, very powerful solution, that is, the Many Models Solution Accelerator (MMSA).

The MMSA lets you build hundreds to thousands of machine learning (ML) models at once and easily scales to hundreds of thousands of models. It's an advanced technology at the cutting edge of ML. Not only can you build hundreds of thousands of models, but you can also use the MMSA to easily deploy them into production.

In this chapter, you will begin by installing the accelerator and understanding the various use cases to which it applies. You will then run the three sections of the accelerator notebook-by-notebook: prepping data, training models, and forecasting new data...

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