Linked.Archi ML-Enabled Systems SHACL Shapes
SHACL shapes for validating ML-enabled system architecture models. Enforces governance rules: every ML model must have versioning, monitoring, and dataset lineage; every serving infrastructure must have latency SLAs.
Linked.Archi
ML Model Governance Shape
Validates that every ML model has the minimum required governance metadata.
Every ML model must have a human-readable label (skos:prefLabel).
Every ML model must have at least one version recorded.
Every ML model must declare the dataset it was trained on (data lineage).
Every ML model must have a monitoring plan for production observability.
Every ML model should have at least one performance metric recorded.
Every ML model should be registered in a model registry.
Dataset Governance Shape
Validates that every dataset has provenance and quality metadata.
Every dataset must have a human-readable label.
Every dataset must declare its upstream data sources (data lineage).
Serving Infrastructure Shape
Validates that serving infrastructure has operational metadata.
Serving infrastructure must declare which model(s) it serves.
Serving infrastructure should have a latency SLA defined.
Training Pipeline Shape
Validates that training pipelines have required connections.
A training pipeline must declare which model(s) it produces.
ML Integration Contract Shape
Validates that ML/SE integration points have sufficient documentation to prevent the ML Mismatch problem (Lewis et al. 2021).
An integration relationship must have exactly one source.
An integration relationship must have exactly one target.
Integration contracts should document the interface agreement.