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

Variation points – alternatives and extensions to the fundamental workflow

The fundamental workflow we developed here is a simple example. For each step we performed, there are multiple alternatives and extensions to what has been shown. All of Part 2:, Building State-of-the-Art Models at Scale, is dedicated to understanding these alternatives and elaborations and to putting them together to build superior models at scale.

Let's first touch on some key variation points here.

Launching an H2O cluster using the Enterprise Steam API versus the UI (step 1)

In our example, we used the convenience of the Enterprise Steam UI to configure and launch an H2O cluster. Alternatively, we could have used the Steam API from our IDE to do so. See the full H2O Enterprise Steam API documentation at https://docs.h2o.ai/enterprise-steam/latest-stable/docs/python-docs/index.html for the Python API and https://docs.h2o.ai/enterprise-steam/latest-stable/docs/r-docs/index.html for the...

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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

Variation points – alternatives and extensions to the fundamental workflow

Launching an H2O cluster using the Enterprise Steam API versus the UI (step 1)

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Authors (2)

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