How do you implement agentic AI for automated machine learning model monitoring?
Machine learning model monitoring agents track model performance in production and detect degradation requiring retraining. Configure agents with access to prediction logs, ground truth data, and model registries. The agent monitors key metrics including prediction accuracy, latency, throughput, and feature distribution drift. Implement data drift detection that identifies when input feature distributions shift away from training data distributions. Detect concept drift where the relationship between features and targets changes over time. Track prediction bias across different population segments to ensure fairness. Generate model health dashboards showing performance trends and drift indicators. Implement automated retraining triggers when performance metrics fall below configurable thresholds. Support A/B model comparison for evaluating new model versions against production baselines.