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Wilfried Woivré

@woivre.com

Microsoft MVP Azure. Big fan of CloudComputing You can find my blogs here https://woivre.fr and https://woivre.com

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Wilfried Woivré@woivre.com · 6d ago

Operating AI/ML Workloads on Kubernetes: A Headlamp Plugin for Kubeflow https://kubernetes.io/blog/2026/07/13/introducing-headlamp-plugin-for-kubeflow/

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

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.

kubernetes.io

Wilfried Woivré@woivre.com · 6d ago

Kubernetes Dashboard to Headlamp: A Step-by-Step Guide https://kubernetes.io/blog/2026/07/13/kubernetes-dashboard-to-headlamp/

Kubernetes Dashboard to Headlamp: A Step-by-Step Guide

1. Before you start: know what is changingKubernetes Dashboard and Headlamp both show what is running in a cluster, but they work differently. When Headlamp runs on the desktop, it uses your existing kubeconfig to connect to one or more clusters and can be extended with plugins. When Headlamp runs inside a cluster, it uses a Kubernetes ServiceAccount to access the API and follow RBAC rules. Kubernetes Dashboard, in contrast, only runs in-cluster and always relies on service account tokens. Understanding these models early helps you choose the right setup and permissions.

kubernetes.io

Wilfried Woivré@woivre.com · 6d ago

Building a Custom Metrics Exporter for Kubernetes https://kubernetes.io/blog/2026/07/14/custom-metrics-exporter-kubernetes/

Building a Custom Metrics Exporter for Kubernetes

Kubernetes ships with built-in awareness of CPU and memory, but most real-world scaling decisions depend on signals that live entirely outside that narrow window: how many messages are waiting in a queue, how long the last batch job took, how many active WebSocket connections a pod is holding. When the built-in metrics are not enough, a metrics exporter bridges that gap. This post walks through writing one from scratch, packaging it as a container, and wiring it into a cluster so that Prometheus — and ultimately the HorizontalPodAutoscaler — can consume it.

kubernetes.io