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

Section 4 – Enterprise Stakeholder Perspectives

In this section, we put on our enterprise stakeholder hats and learn how to plan for, deploy, administer, maintain, and secure the H2O at scale platform. You will first understand how Enterprise Steam works to integrate H2O with your enterprise server cluster and security environment. You will then learn how to configure Enterprise Steam to govern users and define the size of their H2O model-building environments, thereby controlling resource consumption and cost on your enterprise cluster. From there, we learn key areas of operations to support model building and deployment with H2O. Finally, we learn the details of H2O architecture and security from multiple architecture views. Data scientists are shown how these activities relate to their own needs.

This section comprises the following chapters:

Chapter 11, The Administrator and Operations Views
Chapter 12, The Enterprise Architect and Security Views

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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

Section 4 – Enterprise Stakeholder Perspectives

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Authors (2)

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