Just like traditional applications development, machine learning involves writing code. One aspect where the two differ is the workflow. While software development follows a fairly linear process (design, develop, and deploy a feature), machine learning is a different beast. You work on a single feature, which is never 100% complete. You constantly run experiments, and re-design your model in depth at a rapid pace. Traditional tests are entirely useless. Validating whether you are on the right track takes minutes, if not hours.
In this talk, we will take the example of a Machine Learning competition we recently participated in, the Kaggle Home Depot competition, to illustrate what "doing Machine Learning" looks like. We will explain the challenges we faced, and how we tackled them, setting up a harness to easily create and run experiments, while keeping our sanity. We will also draw comparisons with traditional software development, and highlight how some ideas translate from one context to the other, adapted to different constraints.