End to End ML with Kubeflow: Scaling with Big & Tiny Data (+ deep learning of course)

Different options exist for persisting the results and using them for live training, and we will explore the trade-offs of the different formats and their corresponding serving/prediction layers. From there we choose two formats and illustrate how to build an auto-scaling reactive serving layer. We'll tie this all together with Kubeflow as well as showing how it can be used with non-Spark systems.
Holden Karau