modelVersion

Modelversion is a formal label that identifies a specific iteration of a machine learning model artifact. It is used to track how a model changes across training runs, evaluations, and deployments, enabling reproducibility and governance in AI systems.

A model version is usually a combination of a unique identifier (such as a hash or UUID) and an optional version tag. Many teams adopt semantic versioning (MAJOR.MINOR.PATCH) to express the extent of changes, while others use timestamp-based or Git-like identifiers. A complete version includes metadata: the version of the training data and its source, the training script or framework version, the hyperparameters, software dependencies, and the environment used to produce the model outcomes. Evaluation metrics, date, and author/owner are typically recorded to document performance and provenance.

The purpose of modelversion is to guarantee that each model can be reproduced, compared, audited, and rolled back if necessary. A model version helps maintain lineage from data to deployment and supports governance, compliance, and incident investigation. It also supports controlled deployment, such as promoting a version from staging to production.

Implementation is typically through a model registry or artifact store, with immutability to prevent accidental changes. A version may be accompanied by an alias (for example, production-1) that points to the current production-ready version, allowing redirection without changing the version tag. Common practice includes keeping a separate record of data version, training run ID, and evaluation results; and automating versioning as part of CI/CD pipelines.

Lifecycle and best practices emphasize treating versions as immutable artifacts, capturing comprehensive metadata, enforcing a stable naming scheme, validating performance before promotion, and maintaining data and model lineage for traceability. Monitor drift and establish re-training triggers as part of governance.