This Kubeflow Pipelines example shows how to build a web app that summarizes GitHub issues using Kubeflow Pipelines to train and serve a model. The pipeline trains a Tensor2Tensor model on GitHub issue data, learning to predict issue titles from issue bodies. It then exports the trained model and deploys the exported model using Tensorflow Serving. The final step in the pipeline launches a web app, which interacts with the TF-Serving instance in order to get model predictions.

The example is designed to run on a Hosted KFP installation, installed via the Cloud Console or via 'standalone' installation instructions, but would also be straightforward to run on a Kubeflow installation with minor changes.

You can follow this example as a codelab: g.co/codelabs/kfp-gis.