ML solutions in production start from data ingestion and extend upto the
actual deployment step. We want this workflow to be scalable, portable
and simple. Containers and kubernetes are great at the former two but not
the latter if you aren't a devops practitioner. We'll explore how you can
leverage the Kubeflow project to deploy best-of-breed open-source systems
for ML to diverse infrastructures.