Detect AI Anomalies: Real-Time Outliers

Detect AI Anomalies: Real-Time Outliers is an intermediate course for MLOps engineers and data scientists tasked with ensuring AI systems are reliable in production. Static alerts fail when data is dynamic, leaving systems vulnerable to silent failures. This course teaches you to build an intelligent early warning system that catches critical issues before they escalate. You will learn to apply statistical methods like Z-score and Exponentially Weighted Moving Average (EWMA) on streaming data to detect sudden outliers with dynamic thresholds. You will then go beyond simple statistics, using unsupervised learning models like Isolation Forest to uncover subtle, complex anomalies that other methods miss. Through hands-on labs, you will master the crucial skill of contextual analysis—learning to differentiate a true system failure from benign data drift. You will tune model parameters to minimize false positives, reduce alert fatigue, and build the robust monitoring pipelines that are the foundation of modern MLOps

• Implement real-time anomaly detection to find critical outliers
• Apply statistical and unsupervised learning methods on streaming data
• Build intelligent early warning systems for dynamic data
• Develop contextual analysis skills to reduce false positives

• Basic familiarity with the domain and terminology
• Readiness for practical exercises