Kekler Interview Series #5: For AI to Operate the Cloud, It Must First Understand the Data

In Cloud Native environments, a single request passes through multiple microservices and infrastructure layers, meaning the point of failure may differ from the actual root cause, limiting the effectiveness of traditional monitoring. The kt cloud Data Platform team is building an intelligent operations model based on Observability that connects scattered data, enabling AI to grasp reliable context.

Metric, Log, and Trace provide different types of information: signs of system anomalies, detailed records at the time of occurrence, and request processing paths, respectively. These data sources must be interconnected so that AI can accurately analyze failure causes and support operators' decision-making.

Previously, operators relied on experience and navigated multiple dashboards to find root causes; now, issues can be tracked and analyzed through a unified data flow. This lays the foundation for adopting AIOps, with the goal of improving the efficiency and accuracy of cloud operations.