Ease.ml in Action: Towards Multi-tenant Declarative Learning Services
Summary: Multi-tenant declarative ML service ease.ml optimizes cross-user model selection to minimize total regret across groups. Declarative UI lets users specify input/output schemas; ease.ml handles data wrangling, pipeline orchestration, and cost-aware execution. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Bojan Karlaš (ETH Zurich)
- 2. Ji Liu (University of Rochester)
- 3. Wentao Wu (Microsoft)
- 4. Ce Zhang (ETH Zurich)
BibTeX Citation
@article{karlas_vldb18,
title = {{Ease.ml in Action: Towards Multi-tenant Declarative Learning Services}},
author = {Karlaš, Bojan and Liu, Ji and Wu, Wentao and Zhang, Ce},
journal = {PVLDB},
series = {{VLDB} '18},
volume = {11},
number = {12},
pages = {2054--2057},
doi = {10.14778/3229863.3236258},
url = {https://doi.org/10.14778/3229863.3236258},
year = {2018}
}Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,244 | SHiFT: An Efficient, Flexible Search Engine for Transfer Learning | 2023 | VLDB | 5.4587712e-05 |
| 9,371 | Towards an Optimized GROUP BY Abstraction for Large-Scale Machine Learning | 2021 | VLDB | 5.275595e-05 |
| 11,629 | Ease.ML: A Lifecycle Management System for MLDev and MLOps | 2021 | CIDR | 5.093636e-05 |
| 11,802 | Ease.ml/snoopy in Action: Towards Automatic Feasibility Analysis for Machine Learning Application Development | 2020 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 1 of 1 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,029 | Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads | 2018 | VLDB | 9.2843642e-05 |
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