training serving skew

The most expensive bug class in production ML: two implementations of 'the same' feature that quietly disagree. The diagnostic that finds it in an afternoon, and the architecture that makes it impossible.

The bug class with no stack trace: the model is fine, the pipelines are green, and production quietly underperforms offline by five points. Why skew survives even feature-store adoption, and the log-and-wait pattern Google-style answers center on.