 Applications generate hundreds of thousands of logs, and so when something goes wrong, there's all of a sudden all these logs that need to be analyzed to determine what is the issue. The sheer scale of things becomes impossible for a human, a developer, or system administrator to handle at all. So we're working on a tool that actually automatically annotates these logs with how anomalous they are compared to what has been seen in the past. From that, we're able to really reduce the space of logs that need to be analyzed by an administrator when something goes wrong, saving companies time, money, and providing the service to their end user. What we're doing as data scientists at Red Hat is working on both supporting the infrastructure that's AI-ready, as well as integrating into the AI infrastructure intelligent applications that can actually increase the productivity of every single user of Red Hat's products.