Modelsdepending

Modelsdepending is a concept in model-driven workflows that emphasizes the explicit specification of dependencies between models, data sources, and services within a computational system. A model here is any computable artifact that produces outputs used by other components, such as predictive models, feature extractors, or data transformers. By treating relationships as a formal dependency graph, modelsdepending supports traceability, reproducibility, and safe evolution of complex pipelines.

Key concepts include:

- Dependencies: other models, datasets, feature stores, APIs

- Graph structure: nodes, edges, and a defined topological order for execution

- Provenance and versioning: tracking model versions, data versions, and training conditions

- Execution and orchestration: scheduling, triggers, and regeneration of downstream components

- Governance and auditing: lineage, access controls, and impact analysis

Applications and use cases:

Modelsdepending is used in MLOps environments, data science platforms, and model registries to manage end-to-end pipelines with clear dependency boundaries. It enables impact analysis when a base model or dataset is updated, and it supports reproducible experiments by capturing the exact dependency graph for a given run.

Benefits and challenges:

Benefits include improved reproducibility, easier debugging, safer upgrades, and stronger governance. Challenges involve handling complex or cyclic dependencies, drift between dependent components, non-deterministic outputs, and the need for robust tooling to capture and query dependency graphs across heterogeneous systems.

See also:

Data lineage, dependency graphs, directed acyclic graphs, model registry, MLOps.