Introduction

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 Subprojects

Kubeflow is composed of multiple open source projects that address different aspects of the AI lifecycle. These projects 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 model training or model serving.

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 sub-projects. 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.

The Kubeflow Mission

Our goal is to make scaling AI models and deploying them to production as simple as possible, by letting Kubernetes do what it’s great at:

• Easy, repeatable, portable deployments on a diverse infrastructure (for example, experimenting on a laptop, then moving to an on-premises cluster or to the cloud).
• Deploying and managing loosely-coupled microservices.
• Scaling based on demand.

Because AI practitioners use a diverse set of tools, one of the key goals is to customize the stack based on user requirements (within reason) and let the system take care of the “boring stuff”. While we have started with a narrow set of technologies, we are working with many different projects to include additional tooling.

Ultimately, we want to have a set of simple manifests that give you an easy to use AI stack anywhere Kubernetes is already running, and that can self configure based on the cluster it deploys into.

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.

Roadmaps

Kubeflow subprojects have individual roadmaps which established by project maintainers:

• Kubeflow Pipelines roadmap
• Kubeflow Trainer roadmap
• Kubeflow SDK roadmap
• Kubeflow Katib roadmap
• Kubeflow Hub roadmap
• Kubeflow Spark Operator roadmap

Kubeflow Community

Kubeflow is a community-led project maintained by the Kubeflow Working Groups under the guidance of the 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.