kubeflow/pipelines
Machine Learning Pipelines for Kubeflow observed · 2026-10-20
Health v2 · maintenance only
98/100
- Activity 100
- Release rhythm 93
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-10-21. Adoption (stars, forks) is never an input.
- gap_med: 8
- age_days: 3084
- days_rel: 46
- days_push: 1
- n_releases_24m: 30
Adoption not part of the score
4248 stars · 2157 forks observed · 2026-10-20
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
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
framework · maturity stable
machine-learning workflow-automation scheduling etl developer-tools machine-learning data-science cloud-computing python cloud self-hosted mlops kubeflow pipeline-orchestration argo-workflows ml-workflows experiment-tracking kubernetes-native containers automation kubernetes docker
10 sources
- readme: https://github.com/kubeflow/pipelines · fetched 2026-10-20 · caa5b3b51dc2
- homepage: https://www.kubeflow.org/docs/components/pipelines/ · fetched 2026-08-29 · c617a7493a81
- site_page: https://www.kubeflow.org/docs · fetched 2026-08-29 · 2d48c8aa19c2
- site_page: https://www.kubeflow.org/docs/about/community · fetched 2026-08-29 · 85bbf5395b12
- site_page: https://www.kubeflow.org/docs/genai · fetched 2026-08-29 · 0bf5a9037279
- site_page: https://www.kubeflow.org/docs/genai/use-cases · fetched 2026-08-29 · bfca00da3bad
- site_page: https://www.kubeflow.org/docs/started · fetched 2026-08-29 · 52d4becf41e9
- site_page: https://www.kubeflow.org/docs/started/introduction · fetched 2026-08-29 · 240c9135c386
- site_page: https://www.kubeflow.org/docs/started/architecture · fetched 2026-08-29 · eeb497b45570
- site_page: https://www.kubeflow.org/docs/started/installing-kubeflow · fetched 2026-08-29 · a977fcac93bf
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| kubeflow/pipelines | main | 98 |
For agents
markdown · JSON · MCP: product_card(name="kubeflow/pipelines")
Data as of 2026-10-19T08:05:51.212177+00:00 · Report a problem