Yes, workflows can be configured to trigger retraining pipelines when performance drops below thresholds or drift severity crosses configured levels, reducing manual overhead.

Through lightweight telemetry agents embedded into endpoints or cloud environments, the system tracks latency, error rates, input drift, and model response anomalies continuously.

Yes, it integrates with AWS SageMaker, Azure ML, and MLflow using adapters to track lineage, model registry status, and pipeline execution health.

Data pipelines can automatically log and version drift-triggered datasets, tagging them with drift type and operational scenario metadata.

Yes, models are linked with explainability logs and output traceability, ensuring compliance with pharma, chemical, or energy sector audits.

Silent failure detection monitors output variance, signal absence, or no decision changes over time, triggering alerts and fallback logic.