<p>I'm beginning to dig into kubeflow pipelines for a project and have a beginner's question. It seems like kubeflow pipelines work well for training, but how about serving in production?</p>

<p>I have a fairly intensive pre processing pipeline for training and must apply that same pipeline for production predictions. Can I use something like Seldon Serving to create an endpoint to kickoff the pre processing pipeline, apply the model, then to return the prediction? Or is the better approach to just put everything in one docker container?</p>

<p>Yes, you can definitely use Seldon for serving.  In fact, Kubeflow team offers an easy way to link between training and serving: <a href="https://github.com/kubeflow/fairing" rel="nofollow noreferrer">fairing</a></p>

<p>Fairing provides a programmatic way of deploying your prediction endpoint.  You could also take a look at <a href="https://github.com/kubeflow/fairing/tree/master/examples/prediction" rel="nofollow noreferrer">this example</a> on how to deploy your Seldon endpoint with your training result.</p>
