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)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 254 | Snorkel: Rapid Training Data Creation with Weak Supervision | 2018 | VLDB | 0.00030540555 |
| 667 | Incremental Knowledge Base Construction Using DeepDive | 2015 | VLDB | 0.00018440557 |
| 1,532 | Data Management in Machine Learning: Challenges, Techniques, and Systems | 2017 | SIGMOD | 0.00011472681 |
| 2,958 | The Role of Massively Multi-Task and Weak Supervision in Software 2.0 | 2019 | CIDR | 7.8173975e-05 |
| 4,003 | Data Platform for Machine Learning | 2019 | SIGMOD | 6.54347e-05 |
| 5,251 | Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale | 2019 | SIGMOD | 5.6029615e-05 |
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| 13,171 | Reimagining Deep Learning Systems Through the Lens of Data Systems | 2024 | VLDB | - |
| 2,958 | The Role of Massively Multi-Task and Weak Supervision in Software 2.0 | 2019 | CIDR | 7.8173975e-05 |
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| 8,864 | Cerebro: A Layered Data Platform for Scalable Deep Learning | 2021 | CIDR | 4.4326439e-05 |
| 1,482 | Automating Large-Scale Data Quality Verification | 2018 | VLDB | 0.00011725533 |
| 11,313 | Towards Observability for Machine Learning Pipelines | 2022 | CIDR | 4.1945683e-05 |
| 6,115 | An Integrated Development Environment for Faster Feature Engineering | 2014 | VLDB | 5.2042468e-05 |
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| 2,122 | SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle | 2020 | CIDR | 9.4989076e-05 |
| 9,118 | Towards Observability for Production Machine Learning Pipelines | 2022 | VLDB | 4.3928288e-05 |