Operating AI/ML Workloads on Kubernetes: A Headlamp Plugin for Kubeflow

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Kubernetes has quietly become the default platform for AI and machine learning. Whether you run notebook servers for data scientists, schedule distributed training jobs, tune hyperparameters, or orchestrate multi-step ML pipelines, those workloads increasingly land on a Kubernetes cluster. Kubeflow is one of the most popular ways to assemble that stack, and it does so the Kubernetes-native way: every capability is exposed as a Custom Resource Definition (CRD). That design is a gift to cluster operators, because it means ML workloads can be observed and managed with the same primitives as everything else in the cluster. But in practice the specialized ML dashboards that ship with these platforms hide the Kubernetes layer underneath. When a notebook is stuck or a training run fails, the operator is often left dropping back to kubectl to find out what actually happened at the Pod level. This post introduces the Headlamp Kubeflow plugin, which closes that gap by surfacing Kubeflow's custom resources directly inside a general-purpose Kubernetes UI. It is a worked example of a pattern any CRD-heavy platform can follow: meet operators where they already work, and show them the cluster-lev