Building Machine Learning Systems that Understand
Summary: Seamless integration of deep learning models into data management systems and workflows for automated data understanding. Outlines system-level challenges and directions for embedding ML understanding into storage, query processing, and data governance. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jeff Dean (Google)
BibTeX Citation
@inproceedings{dean_sigmod16,
title = {{Building Machine Learning Systems that Understand}},
author = {Dean, Jeff},
series = {{SIGMOD} '16},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2882903.2932259},
url = {https://dl.acm.org/doi/10.1145/2882903.2932259},
year = {2016}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,329 | SliceLine: Fast, Linear-Algebra-based Slice Finding for ML Model Debugging | 2021 | SIGMOD | 8.6268411e-05 |
| 8,017 | ItemSuggest: A Data Management Platform for Machine Learned Ranking Services | 2019 | CIDR | 5.4046581e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 0 of 0 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|
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