Description
First of all it's awesome seeing an initiative to help bring a bit of DevOps to ML, something I feel is well overdue - however I have a question just to clarify my own understanding of what KubeFlow actually is.
The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way for spinning up best of breed OSS solutions.
Regarding this statement in the readme it seems to imply that other OSS solutions will be supported however I'm not sure to what extent, especially regarding TensorFlow. My uncertainty rises from the fact that this is a Google run endeavour and the project name itself (implying the mash up between K8 and TF). As such would it be correct in thinking that other data flow programming libraries are out of this projects scope? (Caffe, CNTK etc)?