Introduction

What is Kubeflow

Kubeflow is the Cloud Native AI platform

Kubeflow is composed of modular, open source projects that form the Kubernetes-native stack for data & AI workloads. Whether you are an AI practitioner, a platform administrator, or a decision-maker, Kubeflow offers modular, scalable, and extensible tools to support your data, AI/ML, and HPC use-cases.

Kubeflow Mission

Kubeflow’s mission is to bridge the Data, AI, and Cloud Native ecosystems. We enable teams to deliver more models, agents, and AI applications into production with well-lit paths across the AI lifecycle. By bringing together the skills and expertise of an open global community, our goal is to be the standard for data & AI workloads on Kubernetes.

The Kubeflow Community and Subprojects embrace the following core principles:

• Simple: Run workloads at any scale without becoming a Kubernetes expert
• Portable: Use the same code on a local laptop, on-premises, or in any cloud
• Scalable: Manage hyperscale training jobs and high-throughput AI agents
• Composable: Mix and match tools across the AI lifecycle

Kubeflow Subprojects

Kubeflow subprojects are designed to be usable both independently and as part of the Kubeflow Distribution. This provides flexibility for users who may not need the full end-to-end AI platform capabilities but want to leverage specific functionalities, such as data processing, model training, or agentic workloads.

You can find list of Kubeflow subprojects in the installation page.

If you are interested to become Kubeflow subproject, this process guidelines.

Kubeflow Ecosystem

Kubeflow has always fostered a strong community-driven culture and actively supports projects that build on, integrate with, or complement Kubeflow subprojects. As part of this effort, the Kubeflow community established the Kubeflow Ecosystem to highlight projects that are valuable to the broader community and demonstrate maturity, sustainability, and excellence within their respective domains.

You can find the list of Kubeflow Ecosystem projects in this page.

If you are interested in joining the Kubeflow Ecosystem, please refer to this process guidelines.

Kubeflow Distribution

The Kubeflow Distribution is a vendor-provided and supported deployment of Kubeflow subprojects and integrations designed to run on specific infrastructure or platform environments. Distributions may include additional tooling, integrations, operational features, and commercial support tailored to the vendor ecosystem.

The Kubeflow Distribution can be installed via Packaged Distributions or Kubeflow Community Distribution.

Kubeflow Community Distribution

Kubeflow Community Distribution (KCD) is community-maintained reference for deploying Kubeflow subprojects and ecosystem integrations in a vendor neutral package.

The development of the KCD is directed by the neutral Kubeflow Distribution Committee (KDC) which is made up of representatives for each Kubeflow subproject and KCD maintainers.

Kubeflow Video Introduction

Watch the following video which provides an introduction to Kubeflow.

Kubeflow History

Kubeflow started as an open sourcing of the way Google ran TensorFlow internally, based on a pipeline called TensorFlow Extended. It began as just a simpler way to run TensorFlow jobs on Kubernetes, but has since expanded to be a foundation of tools for running AI workloads on Kubernetes.

The Kubeflow logo represents the letters K and F inside the heptagon of the Kubernetes logo, which represent two communities: Kubernetes (cloud-native) and flow (Machine Learning). In this context, flow is not only indicating TensorFlow, but also all ML frameworks which make use of Dataflow Graph as the normal form for model/algorithm implementation.

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!

Next Steps

• Follow the installation guide to deploy Kubeflow subprojects or Kubeflow Community Distribution.