Project Overview
Developed a high-performance streaming anomaly detection system capable of processing massive volumes of IoT sensor data in real-time. The system uses ensemble machine learning methods to detect various types of anomalies with exceptional speed and accuracy.
Architecture Highlights: • Stream processing with Apache Kafka and Flink • Ensemble of online learning algorithms • Adaptive thresholding mechanisms • Real-time alerting and visualization • Horizontal scaling capabilities
The system monitors industrial equipment, smart city infrastructure, and environmental sensors. It can detect equipment failures, security breaches, and environmental anomalies within milliseconds of occurrence.
Key Challenges
- Achieving sub-millisecond latency at scale
- Handling concept drift in streaming data
- Balancing sensitivity vs. false positives
Impact & Results
Deployed across 50+ industrial facilities, preventing equipment failures and reducing downtime by 40%. Saved $5M in maintenance costs.