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Kubeflow Fairing is a Python package that streamlines the process of building,training, and deploying machine learning (ML) models in a hybrid cloudenvironment. By using Kubeflow Fairing and adding a few lines of code, you canrun your ML training job locally or in the cloud, directly from Python code ora Jupyter notebook. After your training job is complete, you can use KubeflowFairing to deploy your trained model as a prediction endpoint.

To install the SDK:

pip install kubeflow-fairing

To quick start, you can run theE2E MNIST sample.

To learn how Kubeflow Fairing streamlines the process of training and deployingML models in the cloud, read theKubeflow Fairingdocumentation.

To learn the Kubeflow Fairing SDK API, read theHTML documentation.