Summary

In this chapter, we learned that H2O administrators (working through Enterprise Steam) and the operations team (managing the enterprise server cluster environment where H2O model building is executed, and also managing, monitoring, and governing the models after they are deployed to a scoring environment) are key personas participating in H2O machine learning at scale. We also learned how data scientists who build models are impacted by these personas and why they may need to interact with them.

Let's move on to two additional personas who play a role in H2O machine learning at scale and who may impact the data scientist: the enterprise architect and the security stakeholders.