Troubleshooting is a daily challenge for Kubernetes administrators and developers when deploying applications—constantly monitoring signals and identifying issues. But how can we simplify and improve this process?
In this session, we’ll dive into real-world and common troubleshooting scenarios, demonstrating how you can reduce your Mean Time to Respond, Repair, and Resolve (MTTR). We’ll cover a range of issues—from application-level problems such as memory leaks, unhandled exceptions, configuration errors, and performance bottlenecks, to container-level failures like CrashLoopBackOff and ImagePullBackOff.
By incorporating Large Language Models (LLMs) into common troubleshooting scenarios, we’ll demonstrate how they can be seamlessly integrated as expert assistants—delivering fast, accurate insights to help resolve issues more efficiently and with greater confidence.