This chapter covered a strategy for pipelining and deploying using built-in Scikit-learn methods. It also introduced the pickle module for model persistence and storage, as well as Python-specific concerns at deployment time. I encourage you to return to the code from Chapter 2, Basic Terminology and Our End-to-End Example, and build the entire end-to-end example data mining workflow as a Scikit-learn pipeline.
There's no substitute for practice, so grab some freely available data sets and solve as many real-world problems as you can find. Try your hand at a few analytics competitions and share your code with a friend for review and discussion. Identify the concepts that are toughest for you, and then hunt down explanations from other instructors or authors to get a different viewpoint on the topic. Don't let yourself off the hook until you fully understand the...
