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 KarlasĖ
- 2. Ji Liu
- 3. Wentao Wu
- 4. Ce Zhang
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,182 | SHiFT: An Efficient, Flexible Search Engine for Transfer Learning | 2023 | VLDB | 4.5659133e-05 |
| 9,222 | Towards an Optimized GROUP BY Abstraction for Large-Scale Machine Learning | 2021 | VLDB | 4.3698672e-05 |
| 11,431 | Ease.ML: A Lifecycle Management System for MLDev and MLOps | 2021 | CIDR | 4.1945683e-05 |
| 11,607 | Ease.ml/snoopy in Action: Towards Automatic Feasibility Analysis for Machine Learning Application Development | 2020 | VLDB | 4.1945683e-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 |
|---|---|---|---|---|
| 1,391 | Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads | 2018 | VLDB | 0.0001223506 |
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