Vertica-ML: Distributed Machine Learning in Vertica Database

Summary: Vertica-ML is a distributed ML subsystem embedded in the Vertica database, exposing a SQL-based data science workflow and model management. Models are first-class objects (like tables/views) for versioning and governance; the paper details architecture and performance experiments. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: hd14510f4a41294a8
Venue: SIGMOD
Year: 2020
Pagerank: 8.538263e-05
Overall Rank: 2,386 | 83.96%
DOI: 10.1145/3318464.3386137

Incoming Non-self Citations Over Time

Authors

1. Arash Fard (Vertica)
2. Anh Le (Vertica)
3. George Larionov (Vertica)
4. Waqas Dhillon (Vertica)
5. Chuck Bear (Vertica)

BibTeX Citation

@inproceedings{fard_sigmod20,
        title = {{Vertica-ML: Distributed Machine Learning in Vertica Database}},
        author = {Fard, Arash and Le, Anh and Larionov, George and Dhillon, Waqas and Bear, Chuck},
        series = {{SIGMOD} '20},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3318464.3386137},
        url = {https://dl.acm.org/doi/10.1145/3318464.3386137},
        year = {2020}
}

Incoming Citations (Sorted by Pagerank)

Showing 14 of 14 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
3,240	Towards Demystifying Serverless Machine Learning Training	2021	SIGMOD	7.5002772e-05
4,095	Distributed Deep Learning on Data Systems: A Comparative Analysis of Approaches	2021	VLDB	6.8095767e-05
4,963	Heterogeneity-Aware Distributed Machine Learning Training via Partial Reduce	2021	SIGMOD	6.3384091e-05
5,685	SmartLite: A DBMS-based Serving System for DNN Inference in Resource-constrained Environments	2024	VLDB	6.037958e-05
5,997	BAGUA: Scaling up Distributed Learning with System Relaxations	2022	VLDB	5.9198921e-05
6,458	In-Database Machine Learning with CorgiPile: Stochastic Gradient Descent without Full Data Shuffle	2022	SIGMOD	5.7773842e-05
6,742	Mitigating the Impedance Mismatch between Prediction Query Execution and Database Engine	2025	SIGMOD	5.6910432e-05
8,230	Adda: Towards Efficient in-Database Feature Generation via LLM-based Agents	2025	SIGMOD	5.372368e-05
10,582	NeurStore: Efficient In-database Deep Learning Model Management System	2026	SIGMOD	4.9793485e-05
10,602	TranSQL+: Serving Large Language Models with SQL on Low-Resource Hardware	2026	SIGMOD	4.9793485e-05
11,532	StarfishDB: a Query Execution Engine for Relational Probabilistic Programming	2024	SIGMOD	4.9793485e-05
11,549	Database Native Model Selection: Harnessing Deep Neural Networks in Database Systems	2024	VLDB	4.9793485e-05
11,734	F3 KM: Federated, Fair, and Fast k-means	2023	SIGMOD	4.9793485e-05
11,796	AQUA: Automatic Collaborative Query Processing in Analytical Database	2023	VLDB	4.9793485e-05

Outgoing Citations (Sorted by Pagerank)

Showing 10 of 10 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
12	C-Store: A Column-oriented DBMS	2005	VLDB	0.00068998927
105	The MADlib Analytics Library or MAD Skills, the SQL	2012	VLDB	0.00033638251
145	Neo: A Learned Query Optimizer	2019	VLDB	0.0002908188
179	The Vertica Analytic Database: C-Store 7 Years Later	2012	VLDB	0.00026611886
2,128	Scalable K-Means++	2012	VLDB	8.9964096e-05
2,400	Declarative Recursive Computation on an RDBMS or, Why You Should Use a Database For Distributed Machine Learning	2019	VLDB	8.5213814e-05
2,603	PLANET: Massively Parallel Learning of Tree Ensembles with MapReduce	2009	VLDB	8.2312028e-05
3,683	Cloudy with High Chance of DBMS: A 10-year Prediction for Enterprise-Grade ML	2020	CIDR	7.1006425e-05
3,716	Machine Learning and Databases: The Sound of Things to Come or a Cacophony of Hype?	2015	SIGMOD	7.0726435e-05
7,485	Building the Enterprise Fabric for Big Data with Vertica and Spark Integration	2016	SIGMOD	5.5126823e-05

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