Linked.Archi ML-Enabled Systems Taxonomy
SKOS taxonomy classifying ML-enabled system concepts by lifecycle phase and component role.
Linked.Archi
ML-Enabled Systems Concept Scheme
Classification of ML-enabled system concepts by lifecycle phase and component role.
By Lifecycle Phase
Data Preparation
Phase covering data collection, cleaning, labeling, and feature engineering.
Model Development
Phase covering algorithm selection, training, evaluation, and experiment tracking.
Model Deployment
Phase covering model packaging, serving infrastructure setup, and production release.
Model Monitoring
Phase covering production monitoring, drift detection, and retraining triggers.
By Component Role
Inference Component
Components that perform prediction or inference — models and serving infrastructure.
Data Management Component
Components that manage data flow — pipelines, feature stores, datasets.
Orchestration Component
Components that orchestrate ML workflows — training pipelines, experiment trackers.
Governance Component
Components that support ML governance — model registries, monitoring, audit trails.
By Stakeholder Domain
Analytics Modeling (Data Science)
The domain of data scientists — algorithm selection, model training, evaluation.
Analytics Operations (Data Engineering)
The domain of data engineers — deployment, serving, monitoring, pipeline management.
ML Governance
The domain of ethics officers and compliance — fairness, explainability, privacy, audit.