GraphCanon updated today · GitHub synced today
Trust & integrity
Full report- Maintenance
- Very active (1d since push)
- As of today · Source: github_public_v1
- Provenance
- Not a fork · Organization account
- As of today · Source: github_public_v1
- Security (OSV)
- No lockfile
- As of today · Source: none
Public GitHub metadata and optional OSV dependency scans. Signals, not a guarantee. Trust methodology.
Overview
Machine Learning Toolkit for Kubernetes
Capability facts
No sourced capability facts yet. Facts appear after ingest scans repo manifests (Dockerfile, package.json, MCP configs).
Categories
Tags
README
Kubeflow
What is Kubeflow
Kubeflow is the foundation of tools for AI Platforms on Kubernetes.
AI platform teams can build on top of Kubeflow by using each subproject independently or deploying the entire Kubeflow Community Distribution to meet their specific needs. The Kubeflow Community Distribution is composable, modular, portable, and scalable, backed by an ecosystem of Kubernetes-native projects that cover every stage of the AI lifecycle.
Whether you’re an AI practitioner, a platform administrator, or a team of developers, Kubeflow offers modular, scalable, and extensible tools to support your AI use cases.
Kubeflow consists of Kubeflow Subprojects, Kubeflow Ecosystem, Kubeflow Packaged Distribution, and Kubeflow Community Distribution.
Check out the official documentation for more detailed information.
Repository Role
This repository serves primarily as a gateway to Kubeflow subprojects and shared project metadata. Kubeflow development happens in the individual subproject repositories.
Kubeflow Community
Kubeflow is a community-led project maintained by the Kubeflow Working Groups under the guidance of the Kubeflow Outreach Committee, Kubeflow Distribution Committee, and Kubeflow Steering Committee.
We encourage you to learn about the Kubeflow Community and how to contribute to the project!