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

arXiv: https://arxiv.org/abs/2107.07346 · HTML: https://mlsanity.com/paper/2107.07346/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 15 Jul 2021.

## Builds on (most cited first, 9 of 9)

- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Beyond Accuracy: Behavioral Testing of NLP models with CheckList](https://mlsanity.com/paper/2005.04118/) (2020)
- [Horovod: fast and easy distributed deep learning in TensorFlow](https://mlsanity.com/paper/1802.05799/) (2018)
- [A Transformer-based Embedding Model for Personalized Product Search](https://mlsanity.com/paper/2005.08936/) (2020)
- [Fantastic Embeddings and How to Align Them: Zero-Shot Inference in a Multi-Shop Scenario](https://mlsanity.com/paper/2007.14906/) (2020)
- [Towards Demystifying Serverless Machine Learning Training](https://mlsanity.com/paper/2105.07806/) (2021)
- [SIGIR 2021 E-Commerce Workshop Data Challenge](https://mlsanity.com/paper/2104.09423/) (2021)
- [How to Grow a (Product) Tree: Personalized Category Suggestions for eCommerce Type-Ahead](https://mlsanity.com/paper/2005.12781/) (2020)
- [Shopping in the Multiverse: A Counterfactual Approach to In-Session Attribution](https://mlsanity.com/paper/2007.10087/) (2020)

## Built on by (most cited first, 5 of 5)

- [Contrastive language and vision learning of general fashion concepts](https://mlsanity.com/paper/2204.03972/) (2022)
- [Beyond NDCG: behavioral testing of recommender systems with RecList](https://mlsanity.com/paper/2111.09963/) (2021)
- [DAG Card is the new Model Card](https://mlsanity.com/paper/2110.13601/) (2021)
- [Reasonable Scale Machine Learning with Open-Source Metaflow](https://mlsanity.com/paper/2303.11761/) (2023)
- [(Vector) Space is Not the Final Frontier: Product Search as Program Synthesis](https://mlsanity.com/paper/2304.11473/) (2023)

## 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.