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

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

H2O AI Cloud architecture

We will not dive deep into H2O AI Cloud Architecture but will review three important architecture points:

Components are modular and open: The platform's modular architecture allows enterprises or groups to use the components they need and to hide and ignore the ones they do not. H2O AI Cloud is also open – its components can coexist and interact with the larger enterprise ecosystem, including non-H2O AI/ML components. The MLOps component, for example, can host non-H2O models, such as scikit-learn models, and the AI application Wave SDK can integrate non-H2O APIs with its own.
Cloud-native architecture: H2O AI Cloud is built on a modern Kubernetes architecture that achieves efficient resource consumption among cloud servers. In addition, H2O workloads on the AI Cloud are ephemeral – they spin up when needed, spin down when not in use, and retain state when spinning up again. The H2O AI Cloud also leverages the cloud service providers...

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