Chapter 13: Introducing H2O AI Cloud
In the previous sections of this book, we explored in great detail how to build accurate and trustworthy machine learning (ML) models on massive data volumes using H2O technology, and how to deploy these models for scoring on a diversity of enterprise systems. In doing so, we became familiar with the technologies of H2O Core (H2O-3 and H2O Sparkling Water) and its distributed in-memory architecture to perform model building steps in a horizontally scalable way, using familiar IDEs and languages. We got to know H2O Enterprise Steam as a tool for data scientists to easily provision H2O environments and for administrators to manage users. We learned the technical nature of the H2O MOJO, the ready-to-deploy scoring artifact generated and exported from built models, and we learned a great diversity of patterns for scoring MOJOs on diverse target systems, whether real-time, batch, or streaming. We also learned how enterprise stakeholders beyond data scientists...
