Model lifecycle and observability: Manage model ownership, versioning, traceability, publication, discoverability, effective dating, change control, supersession, and retirement. After implementation, monitor adoption, semantic consistency, anomalies, schema drift, metadata and lineage completeness, data quality, contract compliance, compatibility, realized change impacts, and approved exceptions. Prioritize corrective action and continuous improvement with data owners, data-product owners, platform teams, and delivery backlogs.