Notebooks
Kubeflow includes services to create and manage interactive Jupyter notebooks. You can customize your notebook deployment and your compute resources to suit your data science needs. Experiment with your workflows locally, then deploy them to a cloud when you're ready.
TensorFlow model training
Kubeflow provides a custom TensorFlow training job operator that you can use to train your ML model. In particular, Kubeflow's job operator can handle distributed TensorFlow training jobs. Configure the training controller to use CPUs or GPUs and to suit various cluster sizes.
Model serving
Kubeflow supports a TensorFlow Serving container to export trained TensorFlow models to Kubernetes. Kubeflow is also integrated with Seldon Core, an open source platform for deploying machine learning models on Kubernetes, and NVIDIA TensorRT Inference Server for maximized GPU utilization when deploying ML/DL models at scale.
Pipelines
Kubeflow Pipelines is a comprehensive solution for deploying and managing end-to-end ML workflows. Use Kubeflow Pipelines for rapid and reliable experimentation. You can schedule and compare runs, and examine detailed reports on each run.
Multi-framework
Our development plans extend beyond TensorFlow. We're working hard to extend the support of PyTorch, MXNet, 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.
Community
We are an open and welcoming community of software developers, data scientists, and organizations! Join our Slack workspace for help with any issues you may face, and read more about the community.