Overton: A Data System for Monitoring and Improving Machine-Learned Products

Summary: Overton is a declarative data system that automates the ML lifecycle—training, deployment, fine-grained quality monitoring and error diagnosis—enabling no-code construction of deep-learning production apps. Integrates weak/contradictory supervision, ran at scale, cut errors 1.7–2.9×. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 369
Venue: CIDR
Year: 2020
Pagerank: 7.0040437e-05
Overall Rank: 3,947 | 72.93%
DOI: -

Incoming Non-self Citations Over Time

Authors

1. Christopher Ré (Apple)
2. Feng Niu (Apple)
3. Pallavi Gudipati (Apple)
4. Charles Srisuwananukorn (Apple)

BibTeX Citation

@inproceedings{re_cidr20,
        address = {Amsterdam, Netherlands},
        series = {{CIDR} '20},
        title = {{Overton: A Data System for Monitoring and Improving Machine-Learned Products}},
        booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
        author = {Ré, Christopher and Niu, Feng and Gudipati, Pallavi and Srisuwananukorn, Charles},
        year = {2020}
}

Incoming Citations (Sorted by Pagerank)

Showing 10 of 10 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
2,584	Complaint-driven Training Data Debugging for Query 2.0	2020	SIGMOD	8.3783546e-05
3,589	Ember: No-Code Context Enrichment via Similarity-Based Keyless Joins	2022	VLDB	7.2812353e-05
5,051	Towards Benchmarking Feature Type Inference for AutoML Platforms	2021	SIGMOD	6.385354e-05
5,925	Managing ML Pipelines: Feature Stores and the Coming Wave of Embedding Ecosystems	2021	VLDB	6.0397888e-05
6,538	UPLIFT: Parallelization Strategies for Feature Transformations in Machine Learning Workloads	2022	VLDB	5.8477764e-05
7,687	ExDRa: Exploratory Data Science on Federated Raw Data	2021	SIGMOD	5.5671645e-05
8,523	Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming	2022	VLDB	5.4119882e-05
9,245	Towards Observability for Production Machine Learning Pipelines	2022	VLDB	5.2992628e-05
9,614	Bootleg: Chasing the Tail with Self-Supervised Named Entity Disambiguation	2021	CIDR	5.2444447e-05
11,512	Towards Observability for Machine Learning Pipelines	2022	CIDR	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 6 of 6 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
205	Snorkel: Rapid Training Data Creation with Weak Supervision	2018	VLDB	0.00025235185
579	Incremental Knowledge Base Construction Using DeepDive	2015	VLDB	0.00016217563
1,250	Data Management in Machine Learning: Challenges, Techniques, and Systems	2017	SIGMOD	0.00011485301
3,600	The Role of Massively Multi-Task and Weak Supervision in Software 2.0	2019	CIDR	7.2709969e-05
4,592	Data Platform for Machine Learning	2019	SIGMOD	6.6139071e-05
5,058	Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale	2019	SIGMOD	6.3815523e-05

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