期刊名称：Bulletin of the Technical Committee on Data Engineering
出版年度：2018
卷号：41
期号：4
页码：5-15
出版社：IEEE Computer Society
摘要：The training, maintenance, deployment, monitoring, organization and documentation of machine learning(ML) models – in short model management – is a critical task in virtually all production ML usecases. Wrong model management decisions can lead to poor performance of a ML system and can resultin high maintenance cost. As both research on infrastructure as well as on algorithms is quickly evolving,there is a lack of understanding of challenges and best practices for ML model management. Therefore,this field is receiving increased attention in recent years, both from the data management as well asfrom the ML community. In this paper, we discuss a selection of ML use cases, develop an overviewover conceptual, engineering, and data-processing related challenges arising in the management of thecorresponding ML models, and point out future research directions..
