Summary

In this chapter, we started by understanding what the usual problems are when working with an ML service in production. We understood how the portability of software, as well as ML models, plays an important role in seamless deployments. We also understood how Java’s platform independence makes it good for deployments and how POJOs play a role in it.

Then, we explored what POJOs are and how they are independently functioning objects in the Java domain. We also learned that H2O has provisions to extract models trained by AutoML in the form of POJOs, which we can use as self-contained ML models capable of making predictions.

Building on top of this, we learned how to extract ML models in H2O as POJOs in Python, R, and H2O Flow. Once we understood how to download H2O ML models as POJOs, we learned how to use them to make predictions.

First, we understood that we need the h2o-genmodel.jar library and that it is responsible for interpreting the model POJO in Java...