Modeldepends

Modeldepends is a concept used to describe the explicit tracking of dependencies among models and their surrounding artifacts in machine learning and data science workflows. It encompasses datasets, feature definitions, data preprocessing steps, model versions, hyperparameters, evaluation metrics, and deployment artifacts. The goal is to provide traceability, reproducibility, and governance across experimental runs and production systems.

In practice, modeldepends is represented as a dependency graph that connects each artifact to its prerequisites. A node might be a trained model version, while edges indicate that it was produced from a specific dataset version, a feature engineering script, or a particular hyperparameter setting. This representation supports impact analysis when a dataset or feature changes and helps determine which models need to be retrained or reevaluated.

Tooling around modeldepends often involves metadata stores, model registries, and experiment tracking systems. Integrations with workflow orchestrators enable automated lineage capture during pipeline execution. Users can query the graph to answer questions such as which models were built on a given dataset, or what artifacts would be affected by a feature drift.

While not standardized, best practices emphasize versioned artifacts, immutable identifiers, and clear provenance signals. Limitations include the overhead of maintaining the graph and potential privacy concerns when lineage reveals training data details.

See also: data lineage, model registry, experiment tracking, reproducibility.