Social-technical Systems

Socio-technical systems recognize that AI exists within human, organizational, and technical environments. ModelOp provides centralized governance to manage risk and ensure responsible AI adoption across these intersecting domains.

All Terms
AI Governance
AI Lifecycle Automation
AI Policy
AI Risk
AI Use Case
AI/ML Operationalization
AIMS
AUC
Abstraction
Alerting Rules
Asset
Associated Model
Assurance
Attestation
Audit Trail
Automated Model Validation
Autonomous System
Bias
Black Box AI
Brand Risk
CI/CD for Models
Capability Maturity Assessment
Center of Excellence (CoE)
Champion-Challenger Testing
Conformity Assessment
Confusion Matrix
Containerization
Data Drift
Data Scientist
DataOps
Decision Table
Deployment
DevOps
EU Act
Encapsulation
Enterprise AI
Enterprise AI Architect
Environment/Stage Tagging
Evaluation Metric
Explainability
F1 Score
Fairness
Foundational Model
Heterogeneous Analytics Ecosystem
ITOps
Inferences
Interpretability
LOB Analyst
LOB Executive
Lineage
MLOps
Machine Learning
Metric Function
Model
Model Card
Model Debt
Model Decay
Model Drift
Model Governance
Model Inventory
Model Lifecycle Management
Model Risk Management
ModelOps
ModelOps Engineer
Monitoring SDK
Operational Assurance
Operationalization
Portfolio Intelligence
Predictions
Predictive Models
Production Setting
Programmatic RAI Assessments
ROC Curve
SHAP Values
Schema
Shadow AI
Single Source of Truth
Social-technical Systems
Technical Evidence
Throughput
Time-to-Market
Traceability
Training
Transparency