Introduction to Kubeflow: Fundamentals
- Familiarity with cloud computing environments like AWS, GCP or Azure
- Basic understanding of cloud-native architectures and Kubernetes concepts like pods, controllers, nodes, container images, volumes, etc
- Familiarity with ML concepts like algorithms, model training and parameter tuning
We’ll be covering the following Kubeflow topics in this course:
Machine Learning Workflows
Tools and Add-ons
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.
Who this course is for:
- Data scientists and DevOps with little or no experience with Kubeflow
I have working in open source and data infrastructure for over 20 years. Prior to joining YugabyteDB, I held various positions at InfluxDB, Oracle, MySQL and was a founding member of the Red Hat OpenShift team. Recently, my primary emphasis has been on community building, training and developing technical integrations.