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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

Observing the high-level architecture of H2O

To deep dive into H2O technology, we first need to understand its high-level architecture. It will not only help us understand what the different software components that make up the H2O AI stack are, but it will also help us understand how the components interact with each other and their dependencies.

With this in mind, let’s have a look at the H2O AI high-level architecture, as shown in the following diagram:

Figure 4.1 – H2O AI high-level architecture

The H2O AI architecture is conceptually divided into two parts, each serving a different purpose in the software stack. The parts are as follows:

  • Client layer – This layer points to the client code that communicates with the H2O server.
  • Java Virtual Machine (JVM) components – This layer indicates the H2O server and all of its JVM components that are responsible for the different functionalities of H2O AI, including...
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