Ease.ml/snoopy in Action: Towards Automatic Feasibility Analysis for Machine Learning Application Development
Summary: Feasibility analysis for ML app development via ease.ml/snoopy; predicts whether a target accuracy is achievable on a data distribution. First Bayes error estimator combining NN with pre-trained features; data-management tool for ML viability. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Cedric Renggli (ETH Zurich)
- 2. Luka Rimanic (ETH Zurich)
- 3. Luka Kolar (ETH Zurich)
- 4. Wentao Wu (Microsoft)
- 5. Ce Zhang (ETH Zurich)
BibTeX Citation
@article{renggli_vldb20,
title = {{Ease.ml/snoopy in Action: Towards Automatic Feasibility Analysis for Machine Learning Application Development}},
author = {Renggli, Cedric and Rimanic, Luka and Kolar, Luka and Wu, Wentao and Zhang, Ce},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {12},
pages = {2837--2840},
doi = {10.14778/3415478.3415488},
url = {https://doi.org/10.14778/3415478.3415488},
year = {2020}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,629 | Ease.ML: A Lifecycle Management System for MLDev and MLOps | 2021 | CIDR | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,392 | Northstar: An Interactive Data Science System | 2018 | VLDB | 0.00010936065 |
| 2,029 | Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads | 2018 | VLDB | 9.2843642e-05 |
| 6,965 | Apollo: A Dataset Profiling and Operator Modeling System | 2019 | SIGMOD | 5.7303405e-05 |
| 7,609 | Ease.ml/ci and Ease.ml/meter in Action: Towards Data Management for Statistical Generalization | 2019 | VLDB | 5.5841767e-05 |
| 9,265 | Ease.ml in Action: Towards Multi-tenant Declarative Learning Services | 2018 | VLDB | 5.2965435e-05 |
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