Kubeflow Operations Guide

Chapter 1. Introduction to Kubeflow

Kubeflow is an open source Kubernetes-native platform for developing, orchestrating, deploying, and running scalable and portable machine learning (ML) workloads. It is a cloud native platform based on Google’s internal ML pipelines. The project is dedicated to making deployments of ML workflows on Kubernetes simple, portable, and scalable.

In this book we take a look at the evolution of machine learning in enterprise, how infrastructure has changed, and then how Kubeflow meets the needs of the modern enterprise.

Operating Kubeflow in an increasingly multicloud and hybrid-cloud world will be a key topic as the market grows and as Kubernetes adoption grows. A single workflow may have a life cycle that starts on-premise but quickly requires resources that are only available in the cloud. Building out machine learning tooling on the emergent platform Kubernetes is where life began for Kubeflow, so let’s start there.

Machine Learning on Kubernetes

Kubeflow began life as a basic way to get rudimentary machine learning infrastructure running on Kubernetes. The two driving forces in its development and adoption are the evolution of machine learning in enterprise and the emergence of Kubernetes as the de facto infrastructure management layer.

Let’s take a quick tour of the recent history of machine learning in enterprise to better understand how we got here.

The Evolution of Machine Learning in Enterprise

The past decade has seen the popularity and ...