March 13, 2024, 4:43 a.m. | Xiaoda Wang, Yuan Tang, Tengda Guo, Bo Sang, Jingji Wu, Jian Sha, Ke Zhang, Jiang Qian, Mingjie Tang

cs.LG updates on arXiv.org arxiv.org

arXiv:2403.07608v1 Announce Type: cross
Abstract: Machine Learning (ML) has become ubiquitous, fueling data-driven applications across various organizations. Contrary to the traditional perception of ML in research, ML workflows can be complex, resource-intensive, and time-consuming. Expanding an ML workflow to encompass a wider range of data infrastructure and data types may lead to larger workloads and increased deployment costs. Currently, numerous workflow engines are available (with over ten being widely recognized). This variety poses a challenge for end-users in terms of …

abstract applications arxiv become cloud cs.ai cs.db cs.lg data data-driven data infrastructure infrastructure machine machine learning ml workflow optimization organizations perception research type types workflow workflow optimization workflows

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