Detecting anomalous patterns in real-time 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 Ravishankar Rao Vallabhajosyula, Senior Data Scientist, Gathr and Saurabh Dutta, Technical Product Manager – Gathr, in a discussion on:
Importance of anomaly detection in big data, types of anomalies, and challenges
Prominent real-time anomaly detection application areas
Approaches, techniques and algorithms for big data anomaly detection
Sample implementation of a big data anomaly detection use case on the Gathr platform