# kubeflow/pipelines

Machine Learning Pipelines for Kubeflow

Repository: https://github.com/kubeflow/pipelines
Canonical: https://ross.abutalabs.com/products/pipelines
Homepage: https://www.kubeflow.org/docs/components/pipelines/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: kubeflow-pipelines, mlops, kubeflow, machine-learning, kubernetes, pipeline, data-science
Last push: 2026-08-26T22:19:36+00:00

## Health v2 (maintenance only)
Score: 97/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 92, longevity 100
- inputs: {"age_days": 3036, "days_push": 7, "days_rel": 55, "gap_med": 7, "n_releases_24m": 30}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4193, forks 2093 (observed 2026-08-28T04:08:38.838251+00:00)

## What it is
Kubeflow Pipelines is a Kubernetes-native platform for building, deploying, and orchestrating reusable end-to-end machine learning workflows using the Kubeflow Pipelines SDK (kfp). It provides end-to-end ML orchestration, easy experimentation, and component reuse, running on top of Argo Workflows.

## Use cases
- orchestrate end-to-end machine learning pipelines on kubernetes
- build reusable ml workflow components with a python sdk
- run and manage ml training and data processing experiments
- automate retraining pipelines for production ml models
- build genai pipelines for rag, fine-tuning, and llm evaluation
- schedule and monitor multi-step data science workflows
- deploy a standalone ml pipeline service on a kubernetes cluster

## When to choose
- you run ML workloads on Kubernetes and need scalable, reproducible pipeline orchestration
- you want to compose reusable Python components into end-to-end ML workflows
- you need experiment tracking, parameterized runs, and easy iteration across trials
- you are building GenAI pipelines such as RAG indexing, LLM fine-tuning, or model evaluation
- you want a CNCF-graduated, vendor-supported ecosystem as part of a broader Kubeflow platform

## When to avoid
- you need simple cron-style job scheduling without ML-specific features
- your team has no Kubernetes infrastructure or expertise
- you want a lightweight single-machine pipeline runner with minimal setup
- you only need notebook-based experimentation without production orchestration

## Facets
- artifact type: framework
- maturity: stable
- function: machine-learning, workflow-automation, scheduling, etl, developer-tools
- domain: machine-learning, data-science, cloud-computing
- platform: python, cloud, self-hosted
- tags: mlops, kubeflow, pipeline-orchestration, argo-workflows, ml-workflows, experiment-tracking, kubernetes-native, containers, automation, kubernetes, docker

## Member repositories
- kubeflow/pipelines (main) score 97

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:38.838251+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:22:35.492604+00:00, confidence not recorded.
  - readme: https://github.com/kubeflow/pipelines (fetched 2026-08-28T04:08:38.838251+00:00, sha caa5b3b51dc2)
  - homepage: https://www.kubeflow.org/docs/components/pipelines/ (fetched 2026-08-29T09:13:09.779084+00:00, sha c617a7493a81)
  - site_page: https://www.kubeflow.org/docs (fetched 2026-08-29T09:13:09.788323+00:00, sha 2d48c8aa19c2)
  - site_page: https://www.kubeflow.org/docs/about/community (fetched 2026-08-29T09:13:09.790822+00:00, sha 85bbf5395b12)
  - site_page: https://www.kubeflow.org/docs/genai (fetched 2026-08-29T09:13:09.793128+00:00, sha 0bf5a9037279)
  - site_page: https://www.kubeflow.org/docs/genai/use-cases (fetched 2026-08-29T09:13:09.794982+00:00, sha bfca00da3bad)
  - site_page: https://www.kubeflow.org/docs/started (fetched 2026-08-29T09:13:09.797157+00:00, sha 52d4becf41e9)
  - site_page: https://www.kubeflow.org/docs/started/introduction (fetched 2026-08-29T09:13:09.799244+00:00, sha 240c9135c386)
  - site_page: https://www.kubeflow.org/docs/started/architecture (fetched 2026-08-29T09:13:09.801657+00:00, sha eeb497b45570)
  - site_page: https://www.kubeflow.org/docs/started/installing-kubeflow (fetched 2026-08-29T09:13:09.803554+00:00, sha a977fcac93bf)
- Data as of 2026-08-30T08:39:29.467469+00:00.