> For the complete documentation index, see [llms.txt](https://docs.litwizlabs.ai/wizstudio/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.litwizlabs.ai/wizstudio/kubeflow/kf-pipelines.md).

# KF Pipelines

What is Kubeflow Pipelines?

Kubeflow Pipelines (KFP) is a platform for building and deploying portable and scalable machine learning (ML) workflows using Docker containers.

With KFP you can author [components](https://www.kubeflow.org/docs/components/pipelines/v2/components) and [pipelines](https://www.kubeflow.org/docs/components/pipelines/v2/pipelines) using the [KFP Python SDK](https://pypi.org/project/kfp/), compile pipelines to an [intermediate representation YAML](https://www.kubeflow.org/docs/components/pipelines/v2/compile-a-pipeline#ir-yaml), and submit the pipeline to run on a KFP-conformant backend such as the [open source KFP backend](https://www.kubeflow.org/docs/components/pipelines/v2/installation).

The [open source KFP backend](https://www.kubeflow.org/docs/components/pipelines/v2/installation) is available as a core component of Kubeflow or as a standalone installation. Follow the [installation](https://www.kubeflow.org/docs/components/pipelines/v2/installation) instructions and [Hello World Pipeline](https://www.kubeflow.org/docs/components/pipelines/v2/hello-world) example to quickly get started with KFP.

### Why Kubeflow Pipelines? <a href="#why-kubeflow-pipelines" id="why-kubeflow-pipelines"></a>

KFP enables data scientists and machine learning engineers to:

* Author end-to-end ML workflows natively in Python
* Create fully custom ML components or leverage an ecosystem of existing components
* Easily manage, track, and visualize pipeline definitions, runs, experiments, and ML artifacts
* Efficiently use compute resources through parallel task execution and through caching to eliminating redundant executions
* Maintain cross-platform pipeline portability through a platform-neutral [IR YAML pipeline definition](https://www.kubeflow.org/docs/components/pipelines/v2/compile-a-pipeline#ir-yaml)

### What is a pipeline? <a href="#what-is-a-pipeline" id="what-is-a-pipeline"></a>

A [pipeline](https://www.kubeflow.org/docs/components/pipelines/v2/pipelines) is a definition of a workflow that composes one or more [components](https://www.kubeflow.org/docs/components/pipelines/v2/components) together to form a computational directed acyclic graph (DAG). At runtime, each component execution corresponds to a single container execution, which may create ML artifacts. Pipelines may also feature [control flow](https://www.kubeflow.org/docs/components/pipelines/v2/pipelines/control-flow).

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