How would you design an AI system to manage data versioning effectively?

To manage data versioning effectively in an AI system, I would implement a robust data versioning strategy that includes: 1) Unique identifiers for each dataset version to enable easy retrieval and rollback. 2) A metadata management system to track the lineage of changes, such as who made changes and when. 3) Use of data repositories like DVC (Data Version Control) to manage large datasets, facilitating collaboration across teams. 4) Automated data validation and testing to ensure that the new data versions meet quality standards before deployment. 5) Clear documentation and communication around data changes so that all stakeholders understand the differences between versions.