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.
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Recommended experience
What you'll learn
Apply machine learning techniques to detect anomalies in cybersecurity data such as logs, network traffic, and user behavior.
Automate incident response workflows by integrating AI-driven alerts with security orchestration tools.
Evaluate and fine-tune AI models to reduce false positives and improve real-time threat detection accuracy.
Skills you'll gain
Details to know
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1 assignment
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There are 3 modules in this course
This module introduces anomaly detection from the ground up: what an “anomaly” is, which signals to trust, and how Azure Monitor helps you detect unusual behavior without building a custom ML model. You’ll instrument a workload with Application Insights, explore built-in smart detection, and create your first alert rule using dynamic thresholds and action groups so the right people (or workflows) get notified fast.
What's included
8 readings
This module moves from “something is weird” to “what exactly changed and why.” You’ll learn KQL basics for beginners, then use time-series functions such as make-series and series_decompose_anomalies to detect spikes, dips, and seasonality-aware anomalies in logs. You’ll turn the query into a log alert rule and practice enriching alerts with anomaly scores, dimensions (region/role), and clear troubleshooting steps.
What's included
7 readings
This module turns detection into action. You’ll learn response patterns that are safe and repeatable, then wire Azure Monitor action groups to Logic Apps (and optionally Azure Functions) to notify, create tickets, capture evidence, and trigger a reversible mitigation. You’ll practice parsing the common alert schema so one automation can handle metric, log, and smart detection alerts.
What's included
8 readings1 assignment
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