AI Chat AI Image Generator AI Video Text to Speech

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

07/15/2021

∙

by Jacopo Tagliabue, et al.

∙

∙

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.

page 1

page 2

page 3

page 4

research

∙ 05/01/2017

Towards effective research recommender systems for repositories

In this paper, we argue why and how the integration of recommender syste...

0 Petr Knoth, et al. ∙

research

∙ 10/01/2021

An analysis of open source software licensing questions in Stack Exchange sites

Free and open source software is widely used in the creation of software...

0 Maria Papoutsoglou, et al. ∙

research

∙ 03/21/2023

Reasonable Scale Machine Learning with Open-Source Metaflow

As Machine Learning (ML) gains adoption across industries and new use ca...

0 Jacopo Tagliabue, et al. ∙

research

∙ 09/10/2018

The LKPY Package for Recommender Systems Experiments: Next-Generation Tools and Lessons Learned from the LensKit Project

Since 2010, we have built and maintained LensKit, an open-source toolkit...

0 Michael D. Ekstrand, et al. ∙

research

∙ 09/19/2021

Scaling Enterprise Recommender Systems for Decentralization

Within decentralized organizations, the local demand for recommender sys...

0 Maurits van der Goes, et al. ∙

research

∙ 09/30/2020

MARS-Gym: A Gym framework to model, train, and evaluate Recommender Systems for Marketplaces

Recommender Systems are especially challenging for marketplaces since th...

0 Marlesson R. O. Santana, et al. ∙

research

∙ 09/09/2022

Extending Open Bandit Pipeline to Simulate Industry Challenges

Bandit algorithms are often used in the e-commerce industry to train Mac...

0 Bram van den Akker, et al. ∙