Case study – logistic regression service
As an illustration of the architecture covered previously, let us look at an example of a prediction service that implements a logistic regression model. The model is both trained and scores new data using information passed through URLs (either through the web browser or invoking curl on the command line), and illustrates how these components fit together. We will also examine how we can interactively test these components using the same IPython notebooks as before, while also allowing us to seamlessly deploying the resulting code in an independent application.
Our first task is to set up the databases used to store the information used in modeling, as well as the result and model parameters.
Setting up the database
As a first step in our application, we will set up the database to store our training data and models, and scores obtained for new data. The examples for this exercise consist of data from a marketing campaign, where the objective was to...
