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Machine Learning at Scale with H2O

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

The code and datasets we introduce in this chapter can be found in the GitHub repository at https://github.com/PacktPublishing/Machine-Learning-at-Scale-with-H2O. If you have not set up your H2O environment at this point, see Appendix – Alternative Methods to Launch H2O Clusters for This Book, to do so.

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Authors (2)

Gregory Keys

Gregory Keys

Gregory Keys is a Senior Solution Architect at H2O and has over 20 years of experience designing and implementing software and data systems. He innovated a model deployment and governance framework that was incorporated into Cloudera machine learning product line.

See other products by Gregory Keys

David Whiting

David Whiting

David Whiting is a Data Science Director and Head of Training at H2O.ai. He has over 18 years of experience in business, consulting, and academia. He is adept at developing and maintaining long-term collaborations with experts in multiple fields. He has both led and participated in multi-disciplinary teams and he enjoys mentoring developing analysts and has a substantial experience in doing so.

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