  We argue that immature data pipelines are preventing a large portion of
industry practitioners from leveraging the latest research on recommender
systems. We propose our template data stack for machine learning at "reasonable
scale", and show how many challenges are solved by embracing a serverless
paradigm. Leveraging our experience, we detail how modern open source can
provide a pipeline processing terabytes of data with limited infrastructure
work.
