Our verdict
We have not reviewed Kubeflow Pipelines yet
No one at Enterprise DNA has checked this listing against its sources. The description comes from public listings gathered automatically. Any fact below shows where it came from and when it was last checked.
From the listing
About Kubeflow Pipelines
Kubeflow Pipelines is a platform for building and deploying portable, scalable machine learning pipelines on Kubernetes. It provides a UI and Python SDK to define, schedule, and monitor pipeline runs.
Best for
Teams already using Kubeflow who need a managed way to orchestrate ML pipelines on Kubernetes
Use cases
- Building end-to-end ML training and evaluation pipelines
- Automating model retraining and deployment workflows
- Orchestrating multi-step data processing and feature engineering
Notes
4,151 stars on GitHub. Last updated 2026-06-01. Licensed Apache-2.0.
Pros
- Open source with strong integration into the Kubeflow ecosystem
- Scalable pipeline execution on Kubernetes clusters
- Provides a visual dashboard for tracking pipeline runs and artifacts
Cons
- Requires Kubernetes expertise to set up and maintain
- Steep learning curve for defining complex pipelines
- Community-driven support with limited official documentation
Key facts
What we have checked
- Latest version
- 2.17.3 (released 22 Oct 2026)
- GitHub stars
- 4,227
Each value shows when we last confirmed it and where it came from. Values we have not confirmed are left out, never guessed.
Stars and usage
How Kubeflow Pipelines is growing
4,151 to 4,227
Sources
Where this comes from
- GitHubOfficial
- github api via nightly crawl, recovered from git history 20e874270887c1008f860ee0198e461a23c9e438Registry
A source shows what was published when we checked it. It does not certify security, performance or a working installation. Read our method.
Changelog
What changed
Released 2.17.3
Released sdk-2.17.3
Released 2.17.2
Released 2.17.1
Released 2.17.0
Released sdk-2.17.0-1
Released 2.16.1
Released sdk-2.16.1
Released 2.16.0
Released sdk-2.16.0
Pairs with
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