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.
Ritesh Maheshwari is a performance engineer at LinkedIn, working on making LinkedIn fast using his medley of skills in data and performance analysis, network optimizations, and automation. Before LinkedIn, Ritesh was a performance engineer at Akamai, doing something similar. Ritesh holds a PhD in computer science from Stony Brook University, where he first became passionate about performance while working on computer networks. He is also an alumni of IIT Kharagpur.
Yang Yang is a statistician interested in understanding data and digging signals via statistical modeling. Yang’s work involves developing metrics and methods to track the long-term success of web services and detect key drivers of user engagement and verify them rigorously through statistical analysis. Yang is also keen on developing novel statistical methods to solve real data problems. During her PhD at University of Michigan, Yang proposed new methodologies to analyze panel data with incomplete observations by stochastic optimization techniques and MCMC sampling.
©2016, O'Reilly Media, Inc. • (800) 889-8969 or (707) 827-7019 • Monday-Friday 7:30am-5pm PT • All trademarks and registered trademarks appearing on oreilly.com are the property of their respective owners. • email@example.com