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: 5959
Venue: SIGMOD
Year: 2020
Pagerank: 8.7157552e-05
Overall Rank: 2,347 | 83.90%
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
3,169	Towards Demystifying Serverless Machine Learning Training	2021	SIGMOD	7.6715222e-05
4,067	Distributed Deep Learning on Data Systems: A Comparative Analysis of Approaches	2021	VLDB	6.9293511e-05
4,956	Heterogeneity-Aware Distributed Machine Learning Training via Partial Reduce	2021	SIGMOD	6.4290135e-05
5,706	SmartLite: A DBMS-based Serving System for DNN Inference in Resource-constrained Environments	2024	VLDB	6.1144255e-05
5,876	BAGUA: Scaling up Distributed Learning with System Relaxations	2022	VLDB	6.0557672e-05
6,339	In-Database Machine Learning with CorgiPile: Stochastic Gradient Descent without Full Data Shuffle	2022	SIGMOD	5.907165e-05
6,614	Mitigating the Impedance Mismatch between Prediction Query Execution and Database Engine	2025	SIGMOD	5.8216658e-05
9,155	Adda: Towards Efficient in-Database Feature Generation via LLM-based Agents	2025	SIGMOD	5.3122817e-05
10,386	NeurStore: Efficient In-database Deep Learning Model Management System	2026	SIGMOD	5.093636e-05
10,410	TranSQL+: Serving Large Language Models with SQL on Low-Resource Hardware	2026	SIGMOD	5.093636e-05
11,189	StarfishDB: a Query Execution Engine for Relational Probabilistic Programming	2024	SIGMOD	5.093636e-05
11,209	Database Native Model Selection: Harnessing Deep Neural Networks in Database Systems	2024	VLDB	5.093636e-05
11,420	F3 KM: Federated, Fair, and Fast k-means	2023	SIGMOD	5.093636e-05
11,486	AQUA: Automatic Collaborative Query Processing in Analytical Database	2023	VLDB	5.093636e-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	0.00069513174
106	The MADlib Analytics Library or MAD Skills, the SQL	2012	VLDB	0.00033539462
154	Neo: A Learned Query Optimizer	2019	VLDB	0.00028726181
186	The Vertica Analytic Database: C-Store 7 Years Later	2012	VLDB	0.00026182534
2,085	Scalable K-Means++	2012	VLDB	9.1943614e-05
2,370	Declarative Recursive Computation on an RDBMS or, Why You Should Use a Database For Distributed Machine Learning	2019	VLDB	8.6795925e-05
2,560	PLANET: Massively Parallel Learning of Tree Ensembles with MapReduce	2009	VLDB	8.4143663e-05
3,614	Cloudy with High Chance of DBMS: A 10-year Prediction for Enterprise-Grade ML	2020	CIDR	7.2568185e-05
3,678	Machine Learning and Databases: The Sound of Things to Come or a Cacophony of Hype?	2015	SIGMOD	7.207554e-05
7,344	Building the Enterprise Fabric for Big Data with Vertica and Spark Integration	2016	SIGMOD	5.638371e-05

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