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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 cloud environment. By using Kubeflow Fairing and adding a few lines of code, you can run your ML training job locally or in the cloud, directly from Python code or a Jupyter notebook. After your training job is complete, you can use Kubeflow Fairing to deploy your trained model as a prediction endpoint.

To install the SDK:

pip install kubeflow-fairing

To quick start, you can run the E2E MNIST sample.

To learn how Kubeflow Fairing streamlines the process of training and deploying ML models in the cloud, read the Kubeflow Fairing documentation.

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