What is Kubeflow?
The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable.
Our goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures.
Anywhere you are running Kubernetes, you should be able to run Kubeflow.
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.
Katib is a Kubernetes-native project for automated machine learning (AutoML) with support for hyperparameter tuning, early stopping and neural architecture search.
Kubeflow Training Operator is a unified interface for model training on Kubernetes. It runs scalable and distributed training jobs for popular frameworks including PyTorch, TensorFlow, MPI, MXNet, PaddlePaddle, and XGBoost.