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Automated Machine Learning

You're reading from   Automated Machine Learning Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms

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Product type Paperback
Published in Feb 2021
Publisher Packt
ISBN-13 9781800567689
Length 312 pages
Edition 1st Edition
Languages
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Author (1):
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Adnan Masood Adnan Masood
Author Profile Icon Adnan Masood
Adnan Masood
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Toc

Table of Contents (15) Chapters Close

Preface 1. Section 1: Introduction to Automated Machine Learning
2. Chapter 1: A Lap around Automated Machine Learning FREE CHAPTER 3. Chapter 2: Automated Machine Learning, Algorithms, and Techniques 4. Chapter 3: Automated Machine Learning with Open Source Tools and Libraries 5. Section 2: AutoML with Cloud Platforms
6. Chapter 4: Getting Started with Azure Machine Learning 7. Chapter 5: Automated Machine Learning with Microsoft Azure 8. Chapter 6: Machine Learning with AWS 9. Chapter 7: Doing Automated Machine Learning with Amazon SageMaker Autopilot 10. Chapter 8: Machine Learning with Google Cloud Platform 11. Chapter 9: Automated Machine Learning with GCP 12. Section 3: Applied Automated Machine Learning
13. Chapter 10: AutoML in the Enterprise 14. Other Books You May Enjoy

Chapter 4: Getting Started with Azure Machine Learning

"As a technologist, I see how AI and the fourth industrial revolution will impact every aspect of people's lives."

– Fei-Fei Li, Professor of Computer Science at Stanford University

In the previous chapter, you were introduced to the major AutoML Open Source Software (OSS) tools and libraries. We did a tour of the major OSS offerings, including TPOT, AutoKeras, auto-sklearn, Featuretools, and Microsoft NNI, which will have helped you, the reader, understand the differential value propositions and approaches used in each of these libraries.

In this chapter, we will start exploring the first of many commercial offerings, namely Microsoft's Azure capabilities in automated Machine Learning (ML). Azure Machine Learning is part of the Microsoft AI ecosystem, which helps accelerate the end-to-end ML life cycle using the power of the Windows Azure platform and services. We will start with an...

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