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	"id": "4500af5e-51b2-4a7a-8730-84fdd4bb9386",
	"created_at": "2026-04-06T00:18:02.154128Z",
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	"title": "Overview",
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	"plain_text": "Overview\r\nArchived: 2026-04-05 12:40:42 UTC\r\nWhat is Kubeflow Pipelines?\r\nKubeflow Pipelines (KFP) is a platform for building and deploying portable and scalable machine learning (ML)\r\nworkflows using containers on Kubernetes-based systems.\r\nWith KFP you can author components and pipelines using the KFP Python SDK, compile pipelines to an\r\nintermediate representation YAML, and submit the pipeline to run on a KFP-conformant backend such as the open\r\nsource KFP backend or Google Cloud Vertex AI Pipelines.\r\nThe open source KFP backend is available as a core component of Kubeflow or as a standalone installation. To\r\nuse KFP as part of the Kubeflow platform, follow the instructions for installing Kubeflow. To use KFP as a\r\nstandalone application, follow the standalone installation instructions. To get started with your first pipeline,\r\nfollow the Getting Started instructions.\r\nWhy Kubeflow Pipelines?\r\nKFP enables data scientists and machine learning engineers to:\r\nAuthor end-to-end ML workflows natively in Python\r\nCreate fully custom ML components or leverage an ecosystem of existing components\r\nEasily pass parameters and ML artifacts between pipeline components\r\nEasily manage, track, and visualize pipeline definitions, runs, experiments, and ML artifacts\r\nEfficiently use compute resources through parallel task execution and through caching to eliminating\r\nredundant executions\r\nKeep experimentation and iteration light and Python-centric, minimizing the need to (re)build and maintain\r\ncontainers\r\nMaintain cross-platform pipeline portability through a platform-neutral IR YAML pipeline definition\r\nWhat is a pipeline?\r\nA pipeline is a definition of a workflow that composes one or more components together to form a computational\r\ndirected acyclic graph (DAG). At runtime, each component execution corresponds to a single container execution,\r\nwhich may create ML artifacts. Pipelines may also feature control flow.\r\nNext steps\r\nInstall Kubeflow Pipelines\r\nGetting Started\r\nLearn more about authoring components\r\nhttps://www.kubeflow.org/docs/components/pipelines/overview/pipelines-overview/\r\nPage 1 of 2\n\nLearn more about authoring pipelines\r\nFeedback\r\nWas this page helpful?\r\nThank you for your feedback!\r\nWe're sorry this page wasn't helpful. If you have a moment, please share your feedback so we can improve.\r\nSource: https://www.kubeflow.org/docs/components/pipelines/overview/pipelines-overview/\r\nhttps://www.kubeflow.org/docs/components/pipelines/overview/pipelines-overview/\r\nPage 2 of 2",
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	"language": "EN",
	"sources": [
		"MITRE"
	],
	"references": [
		"https://www.kubeflow.org/docs/components/pipelines/overview/pipelines-overview/"
	],
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		"pipelines-overview"
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