This document describes an example of a pipeline implemented in Google Cloud that performs propensity modeling. It's intended for data engineers, machine learning engineers, or marketing science teams that create and deploy machine learning models. The document assumes that you know machine learning concepts and that you are familiar with Google Cloud, BigQuery, Kubeflow Pipelines, Python, and Jupyter notebooks. It also assumes that you have an understanding of Google Analytics 360 and of the raw export feature in BigQuery
Use Kubeflow Pipelines for propensity modeling on Google Cloud | Cloud Architecture Center
