Machine Learning Pipelines with Kubeflow

A lot of attention is being given now to the idea of Machine Learning Pipelines, which are meant to automate and orchestrate the various steps involved in training a machine learning model; however, it's not always made clear what the benefits are of modeling machine learning workflows as automated pipelines. When tasked with training a new ML model, most Data Scientists and ML Engineers will probably start by developing some new Python scripts or interactive notebooks that perform the data extraction and preprocessing necessary to construct a clean set of data on which to train the model. Then, they might create several additional scripts or notebooks to try out different types of models or different machine learning frameworks. And finally, they'll gather and explore metrics to evaluate how each model performed on a test dataset, and then determine which model to deploy to production. This is obviously an over-simplification of a true machine learning workflow, but the key point is that this general approach requires a lot of manual involvement, and is not reusable or easily repeatable by anyone but the engineer(s) that initially developed it.