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We argue that immature data pipelines are preventing a large portion ofindustry practitioners from leveraging the latest research on recommendersystems. We propose our template data stack for machine learning at "reasonablescale", and show how many challenges are solved by embracing a serverlessparadigm. Leveraging our experience, we detail how modern open source canprovide a pipeline processing terabytes of data with limited infrastructurework.

You Do Not Need a Bigger Boat: Recommendations at Reasonable Scale in a (Mostly) Serverless and Open Stack