Machine Learning (ML) pipelines are complex to set up and even more difficult to maintain when constantly shifting data models are required. In essence, you need a Dynamic ML Pipeline. In this webinar, we will discuss an example ML Pipeline centered around supporting an application that must predict travel times based upon a large data set of taxi ride data. We will walk you through the development of the full ML pipeline using Kubernetes and another Open Source application called KubeDirector. You will learn how to train, register, and finally, query your model for answers. In addition, you will learn how a new capability of KubeDirector called “Connections” enables a dynamic, always up-to-date ML model.