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)

Paper ID: 5346
Venue: SIGMOD
Year: 2016
Pagerank: 5.7309872e-05
Overall Rank: 6,945 | 52.36%
DOI: 10.1145/2882903.2932259

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
2,273	SliceLine: Fast, Linear-Algebra-based Slice Finding for ML Model Debugging	2021	SIGMOD	8.8230899e-05
7,855	ItemSuggest: A Data Management Platform for Machine Learned Ranking Services	2019	CIDR	5.530673e-05

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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