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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

Technical requirements

For this chapter, you will be building models with Python code in Jupyter notebooks through Azure Machine Learning (AML) studio. Furthermore, you will be using datasets and Azure resources that you should have created in previous chapters. As such, the full list of requirements is as follows:

  • Access to the internet
  • A web browser, preferably Google Chrome or Microsoft Edge Chromium
  • A Microsoft Azure account
  • An Azure Machine Learning workspace
  • The titanic-compute-instance compute instance created in Chapter 2, Getting Started with Azure Machine Learning
  • The compute-cluster compute cluster created in Chapter 2, Getting Started with Azure Machine Learning
  • The Titanic Training Data dataset from Chapter 3, Training your First AutoML Model
  • An understanding of how to navigate to the Jupyter environment from an Azure compute instance as demonstrated in Chapter 4, Building an AutoML Regression Solution
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