One post tagged with "data-versioning"

Your model's accuracy dropped 8% in production overnight. Nothing in the model code changed. No deployment happened. The eval suite is green. So you spend a week adjusting hyperparameters, tweaking prompts, comparing checkpoint losses — and eventually someone notices that a schema migration landed three days ago in the feature pipeline. A single field that switched from NULL to an empty string. That's it. That's the regression.

This is the most common failure mode in production ML systems, and it has almost nothing to do with model quality. It has everything to do with a structural gap most teams don't close until they've been burned: data versions and model versions are intimately coupled, but they're tracked by different tools and owned by different teams.