Kubeflow is a machine learning (ML) toolkit that is dedicated to making deployments of ML workflows on Kubernetes simple, portable, and scalable.
Kubeflow pipelines are reusable end-to-end ML workflows built using the Kubeflow Pipelines SDK.
The Kubeflow pipelines service has the following goals:
- End to end orchestration: enabling and simplifying the orchestration of end to end machine learning pipelines
- Easy experimentation: making it easy for you to try numerous ideas and techniques, and manage your various trials/experiments.
- Easy re-use: enabling you to re-use components and pipelines to quickly cobble together end to end solutions, without having to re-build each time.
Get started with your first pipeline and read further information in the Kubeflow Pipelines documentation.
- Getting started with Kubeflow Pipelines (By Amy Unruh)
- How to create and deploy a Kubeflow Machine Learning Pipeline (By Lak Lakshmanan)
Kubeflow pipelines uses Argo under the hood to orchestrate Kubernetes resources. The Argo community has been very supportive and we are very grateful.