Large-scale machine learning systems in real-world industrial settings: A review of challenges and solutions
Reviewartikel, 2020

Background : Developing and maintaining large scale machine learning (ML) based software systems in an in-dustrial 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 qualita-tively analyzed by extracting challenges and solutions. The challenges and solutions were thematically synthe-sized 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.

Machine learning systems

Industrial settings

Challenges

Solutions

SLR

Software engineering

Författare

Lucy Lwakatare

Chalmers, Data- och informationsteknik, Software Engineering, Software Engineering for Cyber Physical Systems

Aiswarya Raj Munappy

Chalmers, Data- och informationsteknik, Software Engineering, Software Engineering for Testing, Requirements, Innovation and Psychology

Ivica Crnkovic

Informations- och kommunikationsteknik

Jan Bosch

Chalmers, Data- och informationsteknik, Software Engineering, Software Engineering for Testing, Requirements, Innovation and Psychology

Helena Holmstrom Olsson

Malmö universitet

Information and Software Technology

0950-5849 (ISSN)

Vol. 127 106368

Ämneskategorier

Produktionsteknik, arbetsvetenskap och ergonomi

Programvaruteknik

Datorsystem

DOI

10.1016/j.infsof.2020.106368

Mer information

Senast uppdaterat

2020-10-30