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

A Reference H2O Wave app as an enterprise AI integration fabric

The low-code Wave SDK allows data scientists, ML engineers, and software developers to build applications that integrate one or more H2O components participating in the ML life cycle into a single application. H2O Wave is thus a powerful integration story.

Two Wave design facts need to be revisited, however, because they make this integration story even more powerful. First, Wave apps are deployed in containers and are thus isolated from other Wave apps. Second, developers can install and integrate publicly available or proprietary Python packages and APIs into the application. This means that H2O Wave apps can integrate both H2O and non-H2O components into a single application. This can effectively be restated as follows: H2O apps can be built as single panes of glass across your entire AI-related enterprise ecosystem. This is shown in the following diagram:

Figure 14.11 – H2O Wave AI app...

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

A Reference H2O Wave app as an enterprise AI integration fabric

Unlock this book and the full library FREE for 7 days

Authors (2)

Personalised recommendations for you