Robust anomaly detection for real user monitoring data
For the past year, LinkedIn has been running and iteratively improving Luminol, its anomaly detection system for real user monitoring data. Ritesh Maheshwari and Yang Yang offer an overview of Luminol, focusing on how to build a low-cost end-to-end system that can leverage any algorithm, and explain lessons learned and best practices that will be useful to any engineering or operations team.
| Talk Title | Robust anomaly detection for real user monitoring data |
| Speakers | Ritesh Maheshwari (LinkedIn), Yang Yang (LinkedIn) |
| Conference | Velocity |
| Conf Tag | Build resilient systems at scale |
| Location | Santa Clara, California |
| Date | June 21-23, 2016 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
For the past year, LinkedIn has been running and iteratively improving Luminol, its anomaly detection system that identifies anomalies in real user monitoring (RUM) data for LinkedIn pages and apps. Ritesh Maheshwari and Yang Yang offer an overview of Luminol, focusing on how to build a low-cost end-to-end system that can leverage any algorithm, and explain lessons learned and best practices that will be useful to any engineering or operations team. LinkedIn will be open sourcing its Python library for anomaly detection and correlation during the talk. Topics include:
