In the high-stakes world of machine learning engineering, the glamour of training state-of-the-art neural networks often masks the grueling reality of daily operations. Seasoned practitioners know that the vast majority of their time is not spent refining model hyperparameters or tweaking architectural layers; it is spent on "data plumbing"—the meticulous, often invisible process of cleaning, […]

