Kubeflow Operations Guide

Chapter 5. Running Kubeflow on Google Cloud

In this chapter we’ll continue to apply concepts and themes from the book so far, but now we’ll shift gears and look at deploying to the cloud. Most organizations will have some sort of preference for one of the three major clouds, based on technology differentiators or dogma around history with a particular vendor. It really comes down to, very often, “which is your organization’s preferred cloud?”

There are many places that offer hosted VMs, but for the purposes of talking about Kubeflow operations we’re going to focus on the “big three” cloud vendors:

• Google Cloud Platform (GCP)
• Azure Cloud Platform
• Amazon Web Services (AWS)

Other vendors offer managed Kubernetes as a service, and are probably good candidates for deploying Kubeflow, but, for the sake of brevity, we’ll focus on the big three clouds. Our focus for each cloud offering is how their managed Kubernetes is deployed, and then what products on the cloud are relevant for integration.

In this chapter, we give a review of the relevant components to Kubeflow operations for Google Cloud, and then point out the specific aspects of the cloud to keep in mind. Kubeflow can run on GCP via the managed Google Kubernetes Engine (GKE). Let’s start off by taking a tour of GCP.

Overview of the Google Cloud Platform

The Google Cloud Platform is a suite of modular services that include:

• Computing
• Data storage
• Data analytics
• Machine learning

These services represent a set of physical assets ...