Yet, security, complexity, and monitoring rank as the topmost challenges in using or deploying containers for heavily cloud-native organizations. According to PerfectScale, common issues, such as not setting memory limits, not properly allocating RAM for pods, or not setting CPU requests, threaten the reliability of Kubernetes. Now, the question is if generative AI can help operators, like platform engineers or site reliability engineers, better interface with the platform.
“Gen AI is not really production grade in most companies,” says Schwartz, who acknowledges its limitations and that it tends to work best in human-in-the-loop scenarios. Nevertheless, he foresees AIOps soon to become a helpful ally for addressing root cause, misconfigurations, and network issues and for guiding optimizations.
Kubernetes-specific, finely-tuned AIs could help operators more quickly diagnose problems, like failed deploys or failed jobs, and tie them to root causes when they arise. “Gen AI is going to take this toil and automate it,” Schwartz says.
