Univariate Anomaly Detection and Change Points in Azure

Every organization collects time-series data, but identifying sudden shifts, performance drops, and unusual patterns before they impact operations is a major challenge. This written course equips you with the fundamental skills to detect anomalies and identify change points in time-series datasets using Azure AI services. Through clear, step-by-step written explanations and practical code examples, you will transition from understanding basic statistical concepts to implementing cloud-based anomaly detection models. You will learn to prepare your data, configure cloud services, and interpret detection results to make data-driven decisions. What you'll learn: First, understand foundational terminology of time-series data, outliers, and change points. Second, configure Azure Cognitive Services for univariate anomaly detection. Third, identify sudden shifts and systemic changes in data trends. Fourth, apply best practices for tuning detection sensitivity and handling missing data points. Fifth, implement modern MLOps monitoring concepts to track model performance over time. Sixth, analyze anomaly detection outputs to trigger automated alerts and workflows. We begin with essential terminology and the mathematical concepts behind anomaly detection before moving on to hands-on configuration of cloud services and modern production practices. This text-based guide ensures you understand both the theory and the practical implementation steps. This course is designed for beginner data analysts, cloud engineers, and developers looking to add anomaly detection to their skill set, with no prior machine learning experience required. Start reading today to master time-series monitoring and safeguard your systems against unexpected changes.