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

Exploring examples of MOJO scoring with H2O software

The patterns in this section represent MOJOs deployed to H2O software. There are many advantages to deploying to H2O software. First, the software is supported by H2O and their team of ML experts. Second, this deployment workflow is greatly streamlined for H2O software since all you have to do is supply the MOJO in a simple upload (via a user interface (UI), an API, or a transfer method such as remote copy). Third, H2O scoring software has additional capabilities—such as monitoring for prediction and data drift—that are important for models deployed to production systems.

Let's start by looking at H2O's flagship model-scoring platform.

H2O MLOps

H2O MLOps is a full-featured platform for deploying, monitoring, managing, and governing ML models. H2O MLOps is dedicated to deploying models at scale (many models and model versions, enterprise-grade throughput and performance, high availability, and so...

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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

Exploring examples of MOJO scoring with H2O software

H2O MLOps

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Authors (2)

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