Machine Learning to Detect Anomalies from Application Logs - Druva

Much of the massive amount of data today is generated by automated systems, and harnessing this information to create value is central to modern technology and business strategies. Machine learning has emerged as a valuable method for many applications--image recognition, natural language processing, robotic control, and much more. By applying machine learning to system-generated debugging logs, we've gained key insights and transformed these logs into critically valuable data sources. Most software products generate logs that are used for root-cause analysis and troubleshooting. Though these logs offer useful insights into real-time performance, mining them for actionable knowledge is challenging.