Detecting anomalous patterns in data can lead to significant actionable insights in a wide variety of application domains, such as fraud detection, network traffic management, predictive healthcare, energy monitoring and many more.
However, detecting anomalies accurately can be difficult. What qualifies as an anomaly is continuously changing and anomalous patterns are unexpected. An effective anomaly detection system needs to continuously self-learn without relying on pre-programmed thresholds.
Join our speakers Dr. Nitin Agarwal, Head- Data Science Practice at Impetus Technologies and Saurabh Dutta, Technical Product Manager - StreamAnalytix, in a discussion on:
- Importance of anomaly detection in enterprise data, types of anomalies, and challenges
- Prominent real-time application areas
- Approaches, techniques and algorithms for anomaly detection
- Sample use-case implementation on the StreamAnalytix platform