Open Source. Battle-Tested. Community Built.
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:
Run workloads at any scale without becoming a Kubernetes expert
Use the same code on a local laptop, on-premises, or in any cloud
Manage hyperscale training jobs and high-throughput AI agents
Mix and match tools across the AI lifecycle
Kubeflow Subprojects
Kubeflow Spark Operator aims to make specifying and running Spark applications as easy and idiomatic as running other workloads on Kubernetes.
Kubeflow Notebooks runs interactive development environments for AI, ML, and Data workloads on Kubernetes.
Kubeflow Trainer is a Kubernetes-native distributed AI platform for scalable LLM fine-tuning and training of AI models across a wide range of frameworks, including PyTorch, MLX, HuggingFace, DeepSpeed, Megatron, JAX, XGBoost, and more.
Kubeflow Katib is a Kubernetes-native project for automated machine learning (AutoML) with support for hyperparameter tuning, early stopping and neural architecture search.
Kubeflow Hub (formerly Model Registry) is a cloud-native component that provides a single pane of glass for ML model developers to index and manage models, versions, and ML artifacts metadata. It fills a gap between model experimentation and production activities.
Kubeflow Pipelines (KFP) is a platform for building then deploying portable and scalable machine learning workflows using Kubernetes.
Kubeflow Central Dashboard is our hub which connects the authenticated web interfaces of Kubeflow and other ecosystem components.
Join our Community
We are an open and welcoming community of software developers, data scientists, and organizations! Check out the weekly community calls, get involved in discussions on the mailing list or chat with others on the Slack Workspace!
