What you'll learn

Anomaly detection models are only as useful as the decisions they drive, and most fail in production due to poor evaluation practices, misaligned metrics, and unmanaged alert fatigue. In this course, Evaluating Anomaly Detection Models, you’ll gain the ability to assess anomaly systems and deploy them with confidence. First, you’ll explore how to choose the right evaluation metrics under class imbalance and interpret precision-recall versus ROC analysis. Next, you’ll discover how to convert raw anomaly scores into reliable alerting decisions using multiple thresholding strategies and alert suppression techniques. Finally, you’ll learn how to monitor systems in production, detect model drift, and build a human-in-the-loop improvement workflow. When you’re finished with this course, you’ll have the skills and knowledge of anomaly detection evaluation needed to build systems that remain accurate and actionable in real-world production environments.