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cs.DB2022★ 5 cited
Management of Machine Learning Lifecycle Artifacts: A Survey
Marius Schlegel, Kai-Uwe Sattler
The explorative and iterative nature of developing and operating machine learning (ML) applications leads to a variety of artifacts, such as datasets, features, models, hyperparame…
cs.DC2022
Blink: Lightweight Sample Runs for Cost Optimization of Big Data Applications
Hani Al-Sayeh, Muhammad Attahir Jibril, Bunjamin Memishi +1
Distributed in-memory data processing engines accelerate iterative applications by caching substantial datasets in memory rather than recomputing them in each iteration. Selecting…