# pipelines

**Repository Path**: alibaba/pipelines

## Basic Information

- **Project Name**: pipelines
- **Description**: Machine Learning Pipelines for Kubeflow
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-10-31
- **Last Updated**: 2026-01-11

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

[![Build Status](https://travis-ci.org/kubeflow/pipelines.svg?branch=master)](https://travis-ci.org/kubeflow/pipelines)
[![Coverage Status](https://coveralls.io/repos/github/kubeflow/pipelines/badge.svg?branch=master)](https://coveralls.io/github/kubeflow/pipelines?branch=master)

## Overview of the Kubeflow pipelines service

[Kubeflow](https://www.kubeflow.org/) is a machine learning (ML) toolkit that is dedicated to making deployments of ML workflows on Kubernetes simple, portable, and scalable. 

**Kubeflow pipelines** are reusable end-to-end ML workflows built using the Kubeflow Pipelines SDK.

The Kubeflow pipelines service has the following goals:

* End to end orchestration: enabling and simplifying the orchestration of end to end machine learning pipelines
* Easy experimentation: making it easy for you to try numerous ideas and techniques, and manage your various trials/experiments.
* Easy re-use: enabling you to re-use components and pipelines to quickly cobble together end to end solutions, without having to re-build each time.

## Documentation

Get started with your first pipeline and read further information in the [Kubeflow Pipelines documentation](https://www.kubeflow.org/docs/guides/pipelines/pipelines-overview/).

## Blog posts

* [Getting started with Kubeflow Pipelines](https://cloud.google.com/blog/products/ai-machine-learning/getting-started-kubeflow-pipelines) (By Amy Unruh)
* How to create and deploy a Kubeflow Machine Learning Pipeline (By Lak Lakshmanan)
  * [Part 1: How to create and deploy a Kubeflow Machine Learning Pipeline](https://towardsdatascience.com/how-to-create-and-deploy-a-kubeflow-machine-learning-pipeline-part-1-efea7a4b650f) 
  * [Part 2: How to deploy Jupyter notebooks as components of a Kubeflow ML pipeline](https://towardsdatascience.com/how-to-deploy-jupyter-notebooks-as-components-of-a-kubeflow-ml-pipeline-part-2-b1df77f4e5b3)

## Acknowledgments

Kubeflow pipelines uses [Argo](https://github.com/argoproj/argo) under the hood to orchestrate Kubernetes resources. The Argo community has been very supportive and we are very grateful.