mlsanityData to 7 Oct 2026

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

arXiv 2107.07346 ↗First posted 15 Jul 2021cs.LGcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 9 of its 24 references that are in the map. First posted on arXiv 15 Jul 2021.

Builds on

9 of 24 references.

References · 9

  1. 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,242
  2. 2020Beyond Accuracy: Behavioral Testing of NLP models with CheckListcited by 979
  3. 2018Horovod: fast and easy distributed deep learning in TensorFlowcited by 275
  4. 2020Fantastic Embeddings and How to Align Them: Zero-Shot Inference in a Multi-Shop Scenariocited by 16
  5. 2021Towards Demystifying Serverless Machine Learning Trainingcited by 16
  6. 2020A Transformer-based Embedding Model for Personalized Product Searchcited by 15
  7. 2021SIGIR 2021 E-Commerce Workshop Data Challengecited by 11
  8. 2020How to Grow a (Product) Tree: Personalized Category Suggestions for eCommerce Type-Aheadcited by 6
  9. 2020Shopping in the Multiverse: A Counterfactual Approach to In-Session Attributioncited by 5

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Cited by year

Citing papers per year: 2021: 2; 2022: 1; 2023: 2.

5 papers.