Summary

In this chapter, we focused on how we can implement models that have been trained using H2O AutoML in different scenarios using different technologies to make predictions on different kinds of data.

We started by implementing an AutoML leader model in a scenario where we tried to make predictions on data over a web service. We created a simple web service that was hosted on localhost using Spring Boot and the Apache Tomcat web server. We trained the model on data using AutoML, extracted the leader model as a POJO, and loaded that POJO as a class in the web application. By doing this, the application was able to use the model to make predictions on the data that it received as a POST request, responding with the prediction results.

Then, we looked into another design pattern where we aimed to make predictions on real-time data. We had to implement a system that can simulate the real-time flow of data. We did this with Apache Storm. First, we dived deep into understanding...