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Course Number
kubeflow101-
Classes Start
Self-Paced
About This Course
In this course, you will learn:
- The basic Kubeflow architecture as well as what components make up the Kubeflow platform and how they work.
- What tools and add-ons are available to enhance the Kubeflow platform as well as how to install Kubeflow on AWS, GCP and locally.
Instructor Led Option
This course is available on a monthly basis with an instructor if you would prefer to take the course live. If this is your preference please navigate and sign up here .
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” 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.
Requirements
We assume that you have basic familiarity with cloud computing environments like AWS, GCP or Azure as well as a basic understanding of cloud-native architectures and Kubernetes concepts like pods, controllers, nodes, container images, volumes, etc. Additionally we assume that you have familiarity with ML concepts like algorithms, model training and parameter tuning