Feature stores, lakehouses and the unglamorous work behind reliable ML

A model trained on features computed one way and served with features computed another way will behave unexpectedly. Sharing feature definitions between training and serving removes the most common cause.

Models assume their inputs arrive on time and in a known shape. Data contracts, which state schema, meaning and freshness expectations, turn silent failures into explicit alerts.

When a prediction is questioned, teams must be able to trace it to a model version, feature values and source data. Lineage should be captured automatically rather than reconstructed afterwards.