You're reading from Machine Learning at Scale with H2O A practical guide to building and deploying machine learning models on enterprise systems

Product type Paperback

Published in Jul 2022

Publisher Packt

ISBN-13 9781800566019

Length 396 pages

Edition 1st Edition

Tools

H2O

Concepts

Machine Learning

Authors (2):

Gregory Keys

David Whiting

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Table of Contents (22) Chapters

Preface

1. Section 1 – Introduction to the H2O Machine Learning Platform for Data at Scale

2. Chapter 1: Opportunities and Challenges FREE CHAPTER

3. Chapter 2: Platform Components and Key Concepts

4. Chapter 3: Fundamental Workflow – Data to Deployable Model

5. Section 2 – Building State-of-the-Art Models on Large Data Volumes Using H2O

6. Chapter 4: H2O Model Building at Scale – Capability Articulation

7. Chapter 5: Advanced Model Building – Part I

8. Chapter 6: Advanced Model Building – Part II

9. Chapter 7: Understanding ML Models

10. Chapter 8: Putting It All Together

11. Section 3 – Deploying Your Models to Production Environments

12. Chapter 9: Production Scoring and the H2O MOJO

13. Chapter 10: H2O Model Deployment Patterns

14. Section 4 – Enterprise Stakeholder Perspectives

15. Chapter 11: The Administrator and Operations Views

16. Chapter 12: The Enterprise Architect and Security Views

17. Section 5 – Broadening the View – Data to AI Applications with the H2O AI Cloud Platform

18. Chapter 13: Introducing H2O AI Cloud

19. Chapter 14: H2O at Scale in a Larger Platform Context

20. Other Books You May Enjoy

Appendix : Alternative Methods to Launch H2O Clusters

H2O AI Cloud component breakdown

Let's take a deeper dive into each of the components.

DistributedML (H2O-3 and H2O Sparkling Water)

DistributedML has been the focus of model building for this book, where it is called H2O Core to represent either H2O-3 or Sparkling Water in that context. Fundamentally, you use H2O Core to build models on massive datasets.

For the purposes of this chapter, the main features and capabilities are presented in the upcoming subsection For more details, see Chapter 2, Platform Components and Key Concepts, to review the distributed in-memory architecture that enables model building on a massive scale. See Chapter 4, H2O Model Building at Scale – Capability Articulation, to review its main capabilities in greater detail.

Key features and capabilities

The key features and capabilities of H2O Core (H2O-3 and Sparkling Water) are as follows:

Model building on massive data volumes: H2O Core has an architecture that partitions...

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You're reading from Machine Learning at Scale with H2O A practical guide to building and deploying machine learning models on enterprise systems

Table of Contents (22) Chapters

H2O AI Cloud component breakdown

DistributedML (H2O-3 and H2O Sparkling Water)

Key features and capabilities

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Authors (2)

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