Data traceability and data observability are linked in my opinion. When data observability finds a problem or anomaly, the lineage (another name for Data Provenance), helps make sense of all the signals. There are lots if metrics observed in different parts of a pipeline. Lineage enables the data/ml operations folks correlate different signals by following dependencies chains to root causes. So if a ML model starts producing strange results, the lineage cane be used to help identify the origin -- going back to the features, the data model, the etl, the ingest, or even the source of the data.
At the end of the day, good AI/ML models requires good data!
Data traceability and data observability are linked in my opinion. When data observability finds a problem or anomaly, the lineage (another name for Data Provenance), helps make sense of all the signals. There are lots if metrics observed in different parts of a pipeline. Lineage enables the data/ml operations folks correlate different signals by following dependencies chains to root causes. So if a ML model starts producing strange results, the lineage cane be used to help identify the origin -- going back to the features, the data model, the etl, the ingest, or even the source of the data.
At the end of the day, good AI/ML models requires good data!