Deploying the model

Often in a production environment, deployment is the step where you release your model into the wild and let it run on unforeseen data. However, data mining also produces many local analysis workflows; that don't necessarily need to deploy but do need to be stored and re-loaded later in order to reproduce the analysis. Both of these use cases require what is called model persistence. The term persistence means the model needs to be stored and loaded for later use. Python is an object-oriented language and appropriately scikit-learn uses objects for most of its analysis routines. Storing an object is not as simple as storing a basic text file full of strings. It instead requires a process called serialization to store in a reliable and error-free manner. One of the most popular serialization packages is a Python core library, pickle. It's what we will...