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

The fundamental workflow

Our fundamental workflow will proceed through the following steps:

Launching the H2O cluster (Enterprise Steam UI)
Connecting to the H2O cluster (your IDE from this point onward)
Building the model
Evaluating and explaining the model
Exporting the model for production deployment
Shutting down the H2O cluster

Step 1 – launching the H2O cluster

This step is done from the Enterprise Steam UI. You will select whether you want an H2O-3 or Sparkling Water cluster and then you will configure the H2O cluster behavior, such as the duration of idle time before it times out and terminates and whether you want to save the state at termination so you can restart the cluster and pick up where you left off (this must be enabled by the administrator). Nicely, Enterprise Steam will auto-size the H2O cluster (number of nodes, memory per node, CPUs) based on your data size.

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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

The fundamental workflow

Step 1 – launching the H2O cluster

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Authors (2)

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