Machine Learning

Machine Learning

Pentaho Data Catalog includes ML Models features that allow you to "discover, organize, and manage Machine Learning metadata such as models, versions, experiments, runs, parameters, metrics, and artefacts in a structured and traceable way."

An ML tracking server integrated with a Data Catalog creates automatic audit trails that address key regulatory requirements. Every model training run is linked to specific dataset versions with timestamps, user access logs, and complete data lineage - eliminating manual compliance tracking and reducing regulatory risk.

Real-World Impact For example, a bank building credit scoring models can instantly demonstrate regulatory compliance by showing exactly which data was used, how bias testing was performed, and complete model validation results. When regulators ask about fairness or when customers request data deletion under GDPR, the system provides immediate, comprehensive documentation rather than requiring weeks of manual investigation.

Business Value This infrastructure transforms compliance from a costly burden into an automated capability that actually improves data science quality. Teams spend less time on paperwork and more time on model development, while organizations gain confidence that their ML systems meet regulatory standards and can quickly respond to audits or regulatory inquiries.

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