modelversies

Modelversies are distinct iterations of a machine learning model produced during development, testing, deployment, and maintenance. They are typically tracked in a model registry to support reproducibility, auditing, and governance. Each modelversie is identified by a unique version identifier and accompanied by metadata such as the version of the training data, the code commit, the software environment, hyperparameters, training duration, and evaluation metrics.

Versioning enables reproducibility and accountability by recording the lineage of a model and its artifacts, including the trained weights, configuration files, and data snapshots used for training. It also supports lifecycle management: a modelversie progresses from draft to validated, approved, and promoted to production, and can be retired or deprecated when superseded.

Version schemes commonly include semantic versioning (major.minor.patch) or timestamp-based identifiers, sometimes combined with descriptive tags.

In practice, modelversies are used to compare performance across iterations, facilitate rollback if a newer version underperforms, and govern deployment across environments (development, staging, production). Artifacts often include the model weights, preprocessing steps, feature stores, and the exact training data snapshot.

Challenge areas include data versioning, storage and transfer costs, drift between training and live data, reproducibility of evaluation, privacy and compliance, and access control.

Related concepts include model registry, MLOps, data provenance, and model cards.