The Data Science Pipeline: Where Assumptions Hide

Every data scientist has trained a model that works perfectly in the notebook, then fails in production. The problem rarely lies in the algorithm itself—it’s hidden in upstream decisions about data collection, preprocessing, and validation. This guide reveals where those assumptions compound and how to catch them.