Chapter 14: H2O at Scale in a Larger Platform Context

In the previous chapter, we broadened our view of H2O machine learning (ML) technology by introducing H2O AI Cloud, an end-to-end ML platform composed of multiple model-building engines, an MLOps platform for model deployment, monitoring, and management, a Feature Store for reusing and operationalizing model features, and a low-code software development kit (SDK) for building artificial intelligence (AI) applications on top of these components and hosting them on an app store for enterprise consumption. The focus of this book has been what we have called H2O at scale, or the use of H2O Core (H2O-3 and Sparkling Water) to build accurate and trusted models on massive datasets, H2O Enterprise Steam to manage H2O Core users and their environments, and the H2O MOJO to easily and flexibly deploy models to diverse target environments. We learned that these H2O-at-scale components are natively a part of the larger H2O AI Cloud platform...