After a year of running KubeFlow across multiple client deployments, I can confidently say: the platform that promises end-to-end ML on Kubernetes delivers end-to-end frustration instead. Here's the post-mortem.
Not everyone can 'just use the cloud.' Healthcare data sovereignty, air-gapped networks, public sector paranoia — sometimes you're deploying ML inference on a rack server in a basement. Here's what that actually looks like with MicroK8s.
Every organisation wanted an MLOps platform circa 2020. Most had data scientists running Jupyter notebooks on their laptops and calling it a pipeline. Building an airport for one flight a week.
