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Ease.ml/ci and Ease.ml/meter in Action: Towards Data Management for Statistical Generalization

Summary: ML development lifecycle reframed as data management; presents ease.ml/ci, a continuous integration engine for ML models, and ease.ml/meter, a profiler to curb overfitting. Both focus on the statistical generalization power of validation and test sets, offering principled data-management guidance for ML software engineering. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11911
Venue
VLDB
Year
2019
Pagerank
4.8216981e-05
Overall Rank
7,138 | 50.35%
DOI
10.14778/3352063.3352110

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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
1,350 Northstar: An Interactive Data Science System 2018 VLDB 0.00012431059
1,391 Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads 2018 VLDB 0.0001223506
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