Canonical Managed Kubeflow lands on Azure

Platform engineering team leads are facing a quiet crisis. Your data science teams want Kubeflow for its pipeline orchestration, metadata tracking, and training operators, so you build it for them on Kubernetes. Then day two arrives. Your engineering backlog is swallowed by breaking changes from upstream, Istio configuration complexity, security patching, and storage provisioning bottlenecks. You didn't build an ML platform; you accidentally adopted a full-time infrastructure maintenance program