Using ChatGPT to monitor and optimize OpenShift
In my previous posts, I have talked about using ChatGPT as a tool to learn Kubernetes and as a tool to troubleshoot Kubernetes. In this part, I will complete the mini series, so to speak, with a look at using ChatGPT to monitor and optimize Kubernetes. Operating a Kubernetes cluster carries the primary objective of keeping workloads running at or above their respective service levels, and do so with maximum efficiency. That is, maintain the availability and performance of deployed applications while minimizing cost for infrastructure (compute, storage, networking, etc) and other parts of the environment. And just like with any other aspect of IT, organizations are applying Artificial Intelligence to do this better and faster. Sidenote: I am using Kubernetes and OpenShift interchangeably in these blog posts, because what I write about applies to both. Personally, I am using OpenShift clusters for all my development and testing. That triggered the question for me if this extends to
