(Please notice the new starting time: 2:30pm, half an hour earlier)
The economic and social importance of software systems continues to increase, coinciding with explosive growth in mobile and cloud computing capabilities. Accelerating changes in the scope and complexity of these systems present significant challenges for the individuals and organizations responsible for their management, operation, and security. Machine-generated log data is a crucial resource for understanding software behavior, but the volume, variety, and velocity of log data itself requires new analysis tools. These tools present a major opportunity for the beneficial application of machine learning and data mining technologies. We will discuss example machine learning approaches in this domain along with design patterns for putting these ideas into practice in a user-facing commercial log management solution.
David Andrzejewski is a Data Sciences Engineer at Sumo Logic and co-organizer of the SF Bay Area Machine Learning meetup group. Prior to Sumo Logic, David held a postdoctoral research position working on knowledge discovery at Lawrence Livermore National Laboratory (LLNL). He completed his PhD in Computer Sciences at the University of Wisconsin-Madison in 2010, where he had also previously received an M.S. in Computer Sciences and a B.S. in Computer Engineering, Mathematics and Computer Sciences.