For most platform engineering and ITOps teams, the ability to orchestrate containerized workloads at scale has transformed development organizations. But the dynamic nature of K8s, with constantly shifting microservices, nodes, and dependencies, makes maintaining visibility into what’s actually happening inside your environment a bit challenging, to say the least.
And now, AI workloads are making it even harder. As organizations continue to adopt AI at scale, the complexity of their Kubernetes environments is growing fast. More workloads,…
