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.
* A user interface (UI) for managing and tracking experiments, jobs, and runs.
* An engine for scheduling multi-step ML workflows.
* An SDK for defining and manipulating pipelines and components.
* Notebooks for interacting with the system using the SDK.