工作流程
适应性
可扩展性
软件部署
比例(比率)
计算机科学
软件开发
软件
软件工程
系统工程
数据科学
风险分析(工程)
工程类
数据库
医学
生态学
物理
量子力学
生物
程序设计语言
作者
Lucy Ellen Lwakatare,Aiswarya Raj,Ivica Crnković,Jan Bosch,Helena Holmström Olsson
标识
DOI:10.1016/j.infsof.2020.106368
摘要
Background: Developing and maintaining large scale machine learning (ML) based software systems in an industrial setting is challenging. There are no well-established development guidelines, but the literature contains reports on how companies develop and maintain deployed ML-based software systems. Objective: This study aims to survey the literature related to development and maintenance of large scale ML-based systems in industrial settings in order to provide a synthesis of the challenges that practitioners face. In addition, we identify solutions used to address some of these challenges. Method: A systematic literature review was conducted and we identified 72 papers related to development and maintenance of large scale ML-based software systems in industrial settings. The selected articles were qualitatively analyzed by extracting challenges and solutions. The challenges and solutions were thematically synthesized into four quality attributes: adaptability, scalability, safety and privacy. The analysis was done in relation to ML workflow, i.e. data acquisition, training, evaluation, and deployment. Results: We identified a total of 23 challenges and 8 solutions related to development and maintenance of large scale ML-based software systems in industrial settings including six different domains. Challenges were most often reported in relation to adaptability and scalability. Safety and privacy challenges had the least reported solutions. Conclusion: The development and maintenance on large-scale ML-based systems in industrial settings introduce new challenges specific for ML, and for the known challenges characteristic for these types of systems, require new methods in overcoming the challenges. The identified challenges highlight important concerns in ML system development practice and the lack of solutions point to directions for future research.
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