Automate AI Anomaly Detection and Response

An outage rarely starts with a red dashboard-it starts as a small anomaly: a spike in latency, a surge in failures, or a subtle change in traffic. The faster you detect and respond, the less damage (and stress) you create. In this course, you’ll build an end-to-end anomaly detection and response loop on Azure. You’ll instrument an app with Application Insights, detect unusual behavior with Azure Monitor smart detection, dynamic thresholds, and KQL time-series functions, and then turn alerts into action using action groups and Logic Apps (with optional Azure Functions for custom remediation). You’ll learn a practical workflow: choose the right signal, set guardrails to reduce noise, enrich alerts with context, and automate a consistent response-notify the right channel, capture evidence, and trigger a safe mitigation step. This course is designed for IT professionals, including DevOps engineers, SREs, and Azure administrators, who want to learn how to automate anomaly detection and response workflows in Azure environments. Learners should be familiar with basic Azure Portal navigation, and JSON familiarity is helpful, along with basic monitoring concepts. No ML prerequisite. By the end, you’ll have a reusable blueprint (queries, alert rules, and automation) you can adapt to real systems to catch problems earlier and respond reliably

• Apply machine learning to detect anomalies in security data
• Automate incident response by integrating AI alerts with orchestration tools
• Evaluate and fine-tune AI models to reduce false positives and improve threat detection

• Basic familiarity with cybersecurity terminology
• Readiness for practical exercises or case studies