Introduction to the Pipelines Interfaces

This page introduces the interfaces that you can use to build and run machine learning (ML) workflows with Kubeflow Pipelines.

You can access the Kubeflow Pipelines UI by clicking Pipeline Dashboard on the Kubeflow UI. The Kubeflow Pipelines UI looks like this:

• Run one or more of the preloaded samples to try out pipelines quickly.
• Upload a pipeline as a compressed file. The pipeline can be one that you have built (see how to build a pipeline) or one that someone has shared with you.
• Create and start a run within the experiment. A run is a single execution of a pipeline. See the .
• Explore the configuration, graph, and output of your pipeline run.
• Schedule runs by creating a recurring run.

See the quickstart guide for more information about accessing the Kubeflow Pipelines UI and running the samples.

When building a pipeline component, you can write out information for display in the UI. See the guides to and visualizing results in the UI.

See the for an overview of the ways you can use the SDK to build pipeline components and pipelines.

The Kubeflow Pipelines API is useful for continuous integration/deployment systems, for example, where you want to incorporate your pipeline executions into shell scripts or other systems. For example, you may want to trigger a pipeline run when new data comes in.