Summary

In this chapter, we have come to understand the high-level architecture of H2O and what the different layers that comprise the overall architecture are. We then dived deep into the client and JVM layer of the architecture, where we understood the different components that make up the H2O software stack. Next, keeping the architecture of H2O in mind, we came to understand the flow of interactions that take place between the client and server, where we understood how exactly we command the H2O server to perform various ML activities. We also came to understand how the interactions flow down the architecture stack during model training.

Building on this knowledge, we have investigated the sequence of interactions that take place inside the H2O server during model training. We also looked into how H2O trains models using the job manager to coordinate training jobs and how H2O communicates the status of model training with the user. And, finally, we unboxed H2O AutoML and came...