Summary

In this chapter, we explored how H2O-at-scale technology (H2O-3, H2O Sparkling Water, H2O Enterprise Steam, and the H2O MOJO) expands its capabilities by participating in the larger H2O AI Cloud end-to-end machine learning ML platform. We saw, for example, how H2O-3 and Sparkling Water can gain from initial rapid prototyping and automated feature discovery. Likewise, we saw how H2O-3 and Sparkling Water models can be deployed easily to the H2O MLOps platform where they gain value from its model-scoring, monitoring, and management capabilities. We also saw how H2O AI Feature Store can operationalize features for sharing, both in model building with H2O-3 or Sparkling Water and model scoring on H2O MLOps.

We started exploring the power of H2O's open source low-code Wave SDK, and how data scientists, ML engineers, and software developers can use it to easily create visualizations, analytics, and workflows across H2O components and thus the full ML life cycle. These applications...