Renaming AI training data after model evaluation refers to modifying filenames or metadata identifiers in your dataset collection. While technically possible through file system changes or database updates, this renaming alters only how humans reference the data, not the model's learned representations. The model remains unchanged because it trained using the raw data content, not the original names themselves.

Data scientists might rename files to reflect revised version labels (like "dataset_v2") during MLOps pipeline updates for clearer lineage tracking. Alternatively, files with complex identifiers like raw sensor IDs could be simplified to human-readable labels in manufacturing or medical imaging datasets for annotation clarity, even after initial model validation. Tools like DVC (Data Version Control) or cloud storage metadata interfaces facilitate such renaming.

The advantage lies in improved organization and traceability without retraining. A key limitation is that renaming does not fix underlying data quality flaws or influence model performance, as the model has already internalized patterns from the data's original content during training. Ethically, consistent, meaningful naming conventions prevent version confusion that might lead to inadvertent use of obsolete or inappropriate datasets in production, supporting auditing requirements. Future tooling may better automate metadata synchronization across training and deployment phases.