Version v0.7 of the documentation is no longer actively maintained. The site that you are currently viewing is an archived snapshot. For up-to-date documentation, see the latest version.
A pipeline is a description of a machine learning (ML) workflow, including all of the components in the workflow and how the components relate to each other in the form of a graph. The pipeline configuration includes the definition of the inputs (parameters) required to run the pipeline and the inputs and outputs of each component.
When you run a pipeline, the system launches one or more Kubernetes Pods corresponding to the steps (components) in your workflow (pipeline). The Pods start Docker containers, and the containers in turn start your programs.
After developing your pipeline, you can upload and share it on the Kubeflow Pipelines UI.
- Read an overview of Kubeflow Pipelines.
- Follow the pipelines quickstart guide to deploy Kubeflow and run a sample pipeline directly from the Kubeflow Pipelines UI.
Was this page helpful?
Glad to hear it! Please tell us how we can improve.
Sorry to hear that. Please tell us how we can improve.