Linked.Archi

Linked.Archi ML-Enabled Systems Extension

Viewpoint Definitions

https://meta.linked.archi/ml-systems/onto#

v0.1.0 draft mlsys: Kalin Maldzhanski Linked.Archi Modified: 2026-05-03 License

Extension ontology for modeling ML-enabled system architectures. Provides element types, relationship types, stakeholders, concerns, and viewpoints for describing the ML aspects of software systems — training pipelines, model serving, feature engineering, data lineage, and the integration boundary between ML and non-ML components. Motivated by the gap identified in Moin et al. (2023): existing architecture frameworks lack stakeholders, viewpoints, and model kinds for data scientists, data engineers, and ML engineers. This extension addresses that gap within the Linked.Archi ecosystem. Trademarked names referenced herein are property of their respective owners.

Contents

ML Model Performance

Analytics Modeling

Frames the concerns of data scientists — model architecture, training pipelines, feature engineering, evaluation metrics, and algorithm selection. Views governed by this viewpoint show the ML model landscape: which models exist, what data they consume, how they are trained, and how they perform.
Purpose: Designing
Concerns: ML Model Performance, Data Quality, Explainability, Fairness & Bias
View type: Diagram
Included concepts:
MLModel TrainingPipeline Dataset FeatureStore ExperimentTracker

Model Drift

Analytics Operations

Frames the concerns of data engineers and ML engineers — model deployment, serving infrastructure, monitoring, retraining pipelines, and operational health. Views governed by this viewpoint show the MLOps landscape: how models move from training to production and how they are maintained.
Purpose: Designing, Governing
Concerns: Model Drift, ML Monitorability, ML Artifact Versioning
View type: Diagram
Included concepts:
ServingInfrastructure MonitoringComponent ModelRegistry DataPipeline TrainingPipeline

Fairness & Bias

ML Ethics & Compliance

Frames fairness, explainability, privacy, and regulatory compliance concerns for ML-enabled systems. Views governed by this viewpoint show which models handle sensitive data, what bias mitigation measures are in place, and compliance status with applicable regulations.
Purpose: Governing
Concerns: Fairness & Bias, Explainability, ML Privacy, Adversarial Robustness
View type: Matrix, Catalog
Included concepts:
MLModel Dataset

ML Artifact Versioning

ML Model Catalog

Inventory of all ML models with key attributes — owner, status, performance metrics, dataset lineage, deployment target, and monitoring status. The ML equivalent of a component catalog.
Purpose: Informing, Governing
Concerns: ML Artifact Versioning, ML Model Performance
View type: Catalog
Included concepts:
MLModel ModelRegistry

ML–SE Collaboration

ML System Integration

Shows how ML components integrate with non-ML components — the boundary that Moin et al. identify as the key collaboration challenge. Views governed by this viewpoint show integration contracts, data flows, latency budgets, and team responsibilities at the ML/SE boundary.
Purpose: Designing, Informing
Concerns: ML–SE Collaboration
View type: Diagram, Matrix
Included concepts:
MLComponent IntegratesWith
Source: linked-archi-meta • Linked.Archi