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Practical Automated Machine Learning Using H2O.ai

You're reading from   Practical Automated Machine Learning Using H2O.ai Discover the power of automated machine learning, from experimentation through to deployment to production

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Product type Paperback
Published in Sep 2022
Publisher Packt
ISBN-13 9781801074520
Length 396 pages
Edition 1st Edition
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Author (1):
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Salil Ajgaonkar Salil Ajgaonkar
Author Profile Icon Salil Ajgaonkar
Salil Ajgaonkar
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Table of Contents (19) Chapters Close

Preface 1. Part 1 H2O AutoML Basics
2. Chapter 1: Understanding H2O AutoML Basics FREE CHAPTER 3. Chapter 2: Working with H2O Flow (H2O’s Web UI) 4. Part 2 H2O AutoML Deep Dive
5. Chapter 3: Understanding Data Processing 6. Chapter 4: Understanding H2O AutoML Architecture and Training 7. Chapter 5: Understanding AutoML Algorithms 8. Chapter 6: Understanding H2O AutoML Leaderboard and Other Performance Metrics 9. Chapter 7: Working with Model Explainability 10. Part 3 H2O AutoML Advanced Implementation and Productization
11. Chapter 8: Exploring Optional Parameters for H2O AutoML 12. Chapter 9: Exploring Miscellaneous Features in H2O AutoML 13. Chapter 10: Working with Plain Old Java Objects (POJOs) 14. Chapter 11: Working with Model Object, Optimized (MOJO) 15. Chapter 12: Working with H2O AutoML and Apache Spark 16. Chapter 13: Using H2O AutoML with Other Technologies 17. Index 18. Other Books You May Enjoy

Introduction to POJOs

POJO is a term coined by Martin Fowler, Rebecca Parsons, and Josh Mackenzie in September 2000. It is an ordinary Java object, but what makes it plain old is not what it should do but rather what it should not do.

A Java object can be a POJO in the following circumstances:

  • The Java object does not extend from any class.
  • The Java object does not implement any interfaces.
  • The Java object does not use any annotations from outside.

What these three restrictions lead to is a Java object that is not dependent on any other library or object outside of itself and is self-contained d enough to perform its logic on its own. You can easily embed POJOs in any Java environment due to their portability, and because of Java’s platform independence, they can be run on any machine.

H2O can export trained models in the form of POJOs. These POJO models can then be deployed and used to make predictions on inbound data. The only dependency on using...

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