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.

背景技术：在工业环境中开发和维护基于大规模机器学习（ML）的软件系统具有挑战性。没有完善的开发指南，但文献中包含有关公司如何开发和维护已部署的基于ML的软件系统的报告。

目的：本研究旨在调查与工业环境中基于大型ML的系统的开发和维护相关的文献，以便对从业者面临的挑战进行综合。此外，我们确定了用于解决其中一些挑战的解决方案。

方法：进行了系统的文献综述，确定了72篇与工业环境中基于ML的大规模软件系统的开发和维护相关的论文。通过抽取挑战和解决方案对定性文章进行定性分析。挑战和解决方案在主题上综合为四个质量属性：适应性，可伸缩性，安全性和隐私性。分析是与机器学习工作流程相关的，即数据获取，培训，评估和部署。

结果：我们确定了与在工业环境中（包括六个不同领域）开发和维护基于ML的大规模软件系统有关的总共23个挑战和8个解决方案。挑战最多的是与适应性和可伸缩性有关的报告。安全和隐私挑战的解决方案最少。

结论：工业环境中基于大型ML的系统的开发和维护带来了针对ML的新挑战，并且针对这些类型的系统的已知挑战特征，需要新的方法来克服这些挑战。所确定的挑战突出了机器学习系统开发实践中的重要问题，而缺乏解决方案则为将来的研究指明了方向。