Out of dateThis guide contains outdated information pertaining to Kubeflow 1.0. This guide needs to be updated for Kubeflow 1.1.
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 your pipeline using the Kubeflow Pipelines UI or the Kubeflow Pipelines SDK.
- 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.
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