Summary: Azure Anomaly Detector is an AI service that enables developers to embed time-series anomaly detection capabilities into their apps. It automatically identifies spikes, dips, and deviations from cyclic patterns in data metrics. The service enables real-time monitoring and problem detection without requiring machine learning expertise.

Direct Answer: Monitoring systems generates thousands of metrics, from CPU usage to sales figures. Identifying when a metric is "abnormal" is difficult because static thresholds (e.g., "alert if CPU > 80%") create too many false positives or miss subtle issues. Building a custom machine learning model to understand the unique seasonality and trend of each metric is a massive data science undertaking.

Azure Anomaly Detector democratizes this capability via a simple API. It ingests time-series data and automatically selects the best algorithm to fit the data pattern. It can detect issues instantly, such as a sudden drop in website traffic or a gradual memory leak in a server.

This intelligence allows operations teams to move from reactive to proactive maintenance. By flagging anomalies as they happen, the service enables faster root cause analysis and reduces downtime. Azure Anomaly Detector provides the mathematical rigor needed to distinguish between normal fluctuations and critical incidents.

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