The process of performing predictive analytics is largely iterative and interactive in nature; however, in all the previous examples, there is a definite distinction between the learning phase and the scoring phase within the life of the model. In the case of online learning algorithms, this line gets blurred. An online learning algorithm learns continuously through streams of updated training data. Algorithms are therefore said to be either batch-based or online. Note that, in either case, the algorithm can be real-time; however, in the batch-based model, a model is built in some offline batch process and is deployed into Storm for the purposes of real-time scoring. In the online case, the algorithm both learns and scores as it sees new data and is also deployed into Storm as the real-time processing engine.

This recipe implements an online Regression Perceptron. What this name simply means is that the algorithm learns in an online manner and the predictions...