Why platform teams are swapping DIY Kubeflow for Canonical managed service 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. Your engineering backlog is swallowed by breaking changes from upstream, Istio configuration complexity, security patching, and storage provisioning bottlenecks. You didnt build an ML platform; you accidentally...
