AnacondaCon 2018. When models and data applications are pushed to production, they become brittle black boxes that can and will break. In this talk you’ll learn how to one-up your data science workflow with a little engineering! Or more specifically, about how to to improve the reliability and quality of your data applications... all so that your models won’t break (or at least won’t break as often)! Examples for this session will be in Python 3.6+ and will rely on: logging to allow us to debug and diagnose things while they’re running, Click to develop “beautiful” command line interfaces with minimal boiler-plating, and Pytest to write short, elegant, and maintainable tests.