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

A quick recap of H2O AI Cloud

The goal of this chapter is to explore how H2O at scale, the focus of this book, picks up new capabilities when used as part of the H2O AI Cloud platform. Let's first have a quick review of H2O AI Cloud by revisiting the following diagram, which we encountered in the previous chapter:

Figure 14.1 – Components of the H2O AI Cloud platform

As a quick summary, we see that H2O AI Cloud has four specialized model-building engines. H2O Core (H2O-3, H2O Sparkling Water) represents H2O DistributedML for horizontally scaling model building on massive datasets. H2O Enterprise Steam, in this context, represents a more generalized tool to manage and provision the model-building engines.

We see that the H2O MOJO, exported from H2O Core model building, can be deployed directly to the H2O MLOps model deployment, monitoring, and management platform (though, as seen in Chapter 10, H2O Model Deployment Patterns, the MOJO can be...

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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

A quick recap of H2O AI Cloud

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Authors (2)

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