English

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

Machine Learning 2021-07-16 v1
Authors: Jacopo Tagliabue
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Abstract

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.

Keywords

pipeline optimization recommender system serverless computing

Cite

@article{arxiv.2107.07346,
  title  = {You Do Not Need a Bigger Boat: Recommendations at Reasonable Scale in a (Mostly) Serverless and Open Stack},
  author = {Jacopo Tagliabue},
  journal= {arXiv preprint arXiv:2107.07346},
  year   = {2021}
}

Comments

Manuscript version of a work accepted at RecSys 2021 (camera-ready forthcoming)

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R2 v1 2026-06-24T04:13:50.971Z