Machine Learning at Scale with H2O

Table of Contents

1. Opportunities and Challenges
2. Platform Components and Key Concepts
3. Fundamental Workflow - Data to Deployable Model
4. H2O Model Building at Scale – Capability Articulation
5. Advanced Model Building – Part I
6. Advanced Model Building – Part II
7. Understanding ML Models
8. Putting It All Together
9. Production Scoring and the H2O MOJO
10. H2O Model Deployment Patterns
11. The Administrator and Operations Views
12. The Enterprise Architect and Security Views
13. Introducing the H2O AI Cloud
14. H2O at Scale in a Larger Platform Context
15. Appendix – Alternative Methods to Launch H2O Clusters