​​The Machine Learning Toolkit for Kubernetes

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.

Features:
- Kubeflow includes services to create and manage interactive Jupyter notebooks.
- Kubeflow provides a custom TensorFlow training job operator that you can use to train your ML model.
- Kubeflow supports a TensorFlow Serving container to export trained TensorFlow models to Kubernetes.
- Kubeflow Pipelines is a comprehensive solution for deploying and managing end-to-end ML workflows.
- Our development plans extend beyond TensorFlow. We're working hard to extend the support of PyTorch, Apache MXNet, MPI, XGBoost, Chainer, and more. We also integrate with Istio and Ambassador for ingress, Nuclio as a fast multi-purpose serverless framework, and Pachyderm for managing your data science pipelines.

https://www.kubeflow.org/

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