Data Versioning
The practice of tracking and managing changes to datasets over time, enabling reproducibility, collaboration, and recovery of previous data versions.
What is Data Versioning?
Data versioning records changes made to a dataset so teams can identify which data was used at a particular point in time. Different versions may reflect additions, removals, corrections, labeling changes, or preprocessing updates. In machine learning workflows, data versions can also be linked to specific experiments and model versions, making it easier to reproduce previous results.
Why is Data Versioning Important?
Machine learning models depend heavily on their training data, so changes to datasets can affect model behavior and performance. Data versioning improves traceability, reproducibility, and collaboration by allowing teams to track changes, compare dataset versions, and restore earlier versions when necessary.
Common use cases
Data versioning is commonly used in machine learning pipelines, MLOps, model training, data engineering, collaborative data projects, and reproducible AI workflows.