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

H2O data capabilities during model building

Recall that H2O model building at scale is performed by using H2O 3 or its extension, Sparkling Water, which wraps H2O 3 with Spark capabilities. The H2O 3 API has extensive data capabilities used in the model building process, and the Sparkling Water API inherits these and adds additional capabilities from Spark. These capabilities are broken down into the following three broad categories:

Ingesting data from the source to the H2O cluster
Manipulating data on the H2O cluster
Exporting data from the H2O cluster to an external destination

As emphasized in previous chapters, the H2O cluster architecture (H2O 3 or Sparkling Water) allows model building at an unlimited scale but is abstracted from the data scientist who builds models by coding H2O in the IDE.

H2O data capabilities are overviewed in the following diagram and elaborated subsequently:

Figure 4.1 – The H2O data capabilities...

The rest of the chapter is locked

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 data capabilities during model building

Unlock this book and the full library FREE for 7 days

Authors (2)

Personalised recommendations for you