Packt+ | Advance your knowledge in tech

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

This Appendix will show you how to launch H2O-3 and Sparkling Water clusters on your local machine so that you can run the code samples in this book. We will also show you how to launch H2O-3 clusters in the 90-day free trial environment for the H2O AI Cloud. This trial environment includes Enterprise Steam to launch and manage H2O clusters on Kubernetes infrastructure.

Note on Environments

Architecture: As introduced in Chapter 2, Platform Components and Key Concepts, you will use a client environment (with the H2O-3 or Sparkling Water libraries implemented) to run commands against a remote H2O-3 or Sparkling Water architecture distributed across multiple server nodes on a Kubernetes or Hadoop cluster. For small datasets, however, the architecture can be launched locally as a single process on the same machine as the client.

Versions: Functionality and code samples from this book use the following versions: H2O-3 version...

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

Appendix : Alternative Methods to Launch H2O Clusters

Unlock this book and the full library FREE for 7 days

Authors (2)

Personalised recommendations for you