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

Leveraging H2O Flow to enhance your IDE workflow

H2O Flow is a web-based UI available wherever an H2O cluster is running. Flow is interactive, allowing users to do everything including import data, build models, investigate models, and put models into production. While incredibly easy to use, our experience is that most data scientists (authors included) prefer coding in Python or R to menu-driven interactive interfaces. This section is written for those data scientists: why use Flow when I am a coder?

There are two main reasons:

Monitoring the state of the H2O cluster and the jobs that are being run
Interactive investigation of the data, models, model diagnostics, and more where interactivity is an asset rather than an annoyance

Connecting to Flow

By default, Flow is started on port 54321 of the H2O server as the cluster is launched (this port is configurable at startup). Enter Error! Hyperlink reference not valid. into your browser to open Flow. The Flow...

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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

Leveraging H2O Flow to enhance your IDE workflow

Connecting to Flow

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Authors (2)

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