7.4 Continuous Learning & Feedback Loop

Phera AI’s engines improve over time via feedback loops: • Operator Feedback: Operators can rate the effectiveness of each automated remediation, feeding data back into ML model retraining. • Anomaly Confirmation: Alerts marked as false positives or true incidents refine future anomaly detection thresholds. • Model Retraining: Periodic offline retraining pipelines incorporate the latest operational data, reducing drift and improving prediction accuracy. • Feature Evolution: New playbook templates and AI-driven recommendations are delivered as seamless platform updates, ensuring your infrastructure always benefits from the latest best practices. By unifying ingestion, inference, and automated remediation, Phera AI’s AI & Automation Engines shift your organization from reactive firefighting to strategic foresight—maximizing uptime, securing against risks, and optimizing performance at scale.