Scaling Data Science does not mean Scaling Machines
Summary: Argues that optimizing for machine-level scale and cloud compute cost misses key dimensions of scaling data science—usability and data scientist productivity. Shows this narrow focus yields unfamiliar tools and calls for user-centered metrics beyond raw compute/scale. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Devin Petersohn (University of California Berkeley)
BibTeX Citation
@inproceedings{petersohn_cidr21,
address = {Amsterdam, Netherlands},
series = {{CIDR} '21},
title = {{Scaling Data Science does not mean Scaling Machines}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Petersohn, Devin},
year = {2021}
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