Introduction to Kubeflow: Fundamentals

We’ll be covering the following Kubeflow topics in this course:

• Architecture

• Machine Learning Workflows

• Components

• Tools and Add-ons

• Distributions

• Kubeflow Community

• Certification Prep

What is Kubeflow?

Kubeflow as a project got its start over at Google. The idea was to create a simpler way to run TensorFlow jobs on Kubernetes. So, Kubeflow was created as a way to run TensorFlow, based on a pipeline called TensorFlow Extended and then ultimately extended to support multiple architectures and multiple clouds so it could be used as a framework to run entire machine learning pipelines. The Kubeflow open source project (licensed Apache 2.0) was formally announced at the end of 2017.

In a nutshell, Kubeflow is the machine learning toolkit that runs on top of Kubernetes. Kubeflow’s combined components allow both data scientists and DevOps to manage data, train models, tune and serve them, as well as monitor them.

For whom is the “Introduction to Kubeflow” training and certification series of courses for?

Data scientists, machine learning developers, DevOps engineers and infrastructure operators who have little or no experience with Kubeflow and want to build their knowledge step-by-step, plus test their knowledge and earn certificates along the way.

What are the prerequisites for this course?

A basic understanding of cloud computing, Kubernetes and machine learning concepts is very helpful.