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

View More author details

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

Technical requirements

For this chapter, we will focus on using Enterprise Steam to launch H2O clusters on an enterprise server cluster. Enterprise Steam technically is not required to launch H2O clusters but enterprise stakeholders typically view Enterprise Steam as a security, governance, and administrator requirement for implementing H2O in enterprise environments.

Enterprise Steam requires a license purchased from H2O.ai. If your organization does not have an instance of Enterprise Steam installed, you can access Enterprise Steam and an enterprise server cluster through a temporary trial license of the larger H2O platform. Alternatively, for ease of conducting the exercises in this book, you may wish to launch H2O clusters as a sandbox in your local environment (for example, on your laptop or desktop workstation) and bypass the use of Enterprise Steam.

See Appendix – Alternative Methods to Launch H2O Clusters for this Book to help you decide on how you wish to launch...

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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

Technical requirements

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Authors (2)

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