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 project independently or deploying the
entire AI reference platform to meet their specific needs. The Kubeflow AI reference platform is
composable, modular, portable, and scalable, backed by an ecosystem of Kubernetes-native
projects for each stage of
the AI lifecycle.
Deploy Kubeflow anywhere you run Kubernetes.
Kubeflow Projects
Kubeflow Pipelines (KFP) is a platform for building then deploying portable and scalable machine learning workflows using Kubernetes.
Kubeflow Notebooks lets you run web-based development environments on your Kubernetes cluster by running them inside Pods.
Kubeflow Central Dashboard is our hub which connects the authenticated web interfaces of Kubeflow and other ecosystem components.
Kubeflow Trainer is a Kubernetes-native project for fine-tuning LLMs and model training. It enables scalable and distributed training with various frameworks including PyTorch, JAX, TensorFlow, and more.
Katib is a Kubernetes-native project for automated machine learning (AutoML) with support for hyperparameter tuning, early stopping and neural architecture search.
KServe (previously KFServing) solves production model serving on Kubernetes. It delivers high-abstraction and performant interfaces for frameworks like Tensorflow, XGBoost, ScikitLearn, PyTorch, and ONNX.
Kubeflow 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. It provides a central interface for all stakeholders in the MLOps lifecycle to collaborate on ML models.
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!
