Data Versioning

Data versioning applies version control — the same concept used for code with tools like Git — to datasets. Without it, a training run from two months ago may be impossible to reproduce exactly, because the training data may have changed without a record of what it was at the time. Data versioning makes it possible to trace why a model behaves differently after a data update, to roll back to a previous dataset if a new version causes regressions, and to audit exactly what data was used to train a given model. For behavior architects and ML engineers working on model improvement, data versioning is foundational infrastructure: it’s the basis for being able to reason systematically about the relationship between data decisions and behavioral outcomes.