Microsoft Fabric Thursday Expert Series - 2025

AI-Powered Anomaly Detection for Scalable Cloud Data Quality Management

Ensuring high data quality is critical for reliable analytics and informed decision-making. However, traditional methods for anomaly detection in cloud data pipelines often lack scalability and precision. This session presents a practical AI-powered framework designed to automate and enhance anomaly detection in modern cloud-native environments.

Using advanced machine learning techniques—including supervised models and unsupervised anomaly detection—the system continuously monitors data streams, learns from historical patterns, and accurately flags anomalies in real time. It significantly reduces false positives and adapts to evolving pipeline behaviors across diverse architectures.

This approach integrates seamlessly with scalable data platforms, offering a flexible solution for enterprise-scale data quality management. Real-world applications have shown improvements in issue detection, reduced latency, and operational efficiency gains by enabling faster remediation and freeing up engineering resources for strategic initiatives.

Attendees will gain insights into model design, deployment strategies, and lessons learned from production implementations. This talk highlights how AI can transform data reliability practices and drive better outcomes through intelligent automation in the cloud.