Kubeflow deployment on GCP

Question

I have been reading for few weeks for different approaches for ML in production. I decided to test Kubeflow and I decided to test it on GCP. I started to deploy Kubeflow on GCP using the guiidline on official kubeflow website(here https://www.kubeflow.org/docs/gke/). I run into a lot of issues and it was quit hard to fix them. I started to look into a better approach and I noticed that GCP AI platform now offers deploying Kubeflow pipelines with just few simple steps. (https://cloud.google.com/ai-platform/pipelines/docs/connecting-with-sdk.)

After easily setting up this, I had few question and doubts. If it is this much easy to set up and deploy Kubeflow why we have to go through such a cumbersome way as suggested in the kubeflow official website. Since creating Kubeflow pipeline on GCP means basically I am deploying Kubeflow on GCP, does that mean I can access other Kubeflow services like Katib?

Elnaz

Answer 1

The kubeflow official website provides the required information in detailed way and where as in google cloud it directly provides you the services with possible ready solution.

Referring to will fuks document it says YES, you can able to access katlib on GCP

Answer 2

The GCP managed service of Kubeflow Pipelines is just that. You won't have a lot of access to the cluster to make changes. I've deployed a Kubeflow cluster that can still reach the AI Hub as well.

I believe they have plans to expand what can be deployed in the AI Platform but if you don't want to wait, the self-deployment is possible (but not easy) IMO.