Stages ingest, validate, clean, join, filter, version, and publish records on a schedule or event trigger. Monitoring catches schema changes, missing partitions, drift, and quality failures before they reach training or serving.
A data pipeline moves and transforms data from sources into datasets or features that models can use.
Stages ingest, validate, clean, join, filter, version, and publish records on a schedule or event trigger. Monitoring catches schema changes, missing partitions, drift, and quality failures before they reach training or serving.