Yes, thank you from me too! Wrapping up a project where we ended up deploying a self hosted version on EKS and leveraging its autoscaling abilities. Ticked a lot boxes for our team from model deployment to running pipelines, tracking experiments, storing artifacts and even allowing the deployment of some R code/models by making the use of custom docker images a breeze 😅
Given once your pipes become sufficiently complex and start to veer more outside the ML domain, you might opt in for some other solution to be run in parallel, e.g., Airflow, Dagster, argoflow, ... out of which the latter acts as a backbone for Kubeflow pipes.
Would be happy to recommend ClearML and even the enterprise versions for large enough orgs : )