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Architecting ML-enabled systems: challenges, best practices, and design decisions
Publication Type:
Journal article
Venue:
Journal of Systems and Software
Abstract
Context. Machine learning is increasingly used in a wide set of applications ranging from recommendation engines to
autonomous systems through business intelligence and smart assistants. Designing and developing machine learning
systems is a complex process that can be eased by leveraging effective design decisions tackling the most important
challenges and by having a good system and software architecture.
Goal. The research goal of this work is to identify common challenges, best design practices, and main software architecture
design decisions of machine learning enabled systems from the point of view of researchers and practitioners.
Method. We performed a mixed method including a systematic literature review and expert interviews. We started with
a systematic literature review. From an initial set of 3038 studies, we selected 41 primary studies, which we analysed
according to a data extraction, analysis, and synthesis process. In addition, we conducted 12 expert interviews that
involved researchers and professionals with machine learning expertise from 9 different countries.
Findings. We identify 35 design challenges, 42 best practices and 27 design decisions when architecting machine learning
systems. By eliciting main design challenges, we contribute to best practices and design decisions. In addition, we identify
correlations among design challenges, decisions and best practices.
Conclusions. We believe that practitioners and researchers can benefit from this first and comprehensive analysis of
current software architecture design challenges, best practices, and design decisions
Bibtex
@article{Nazir6777,
author = {Roger Nazir and Alessio Bucaioni and Patrizio Pelliccione},
title = {Architecting ML-enabled systems: challenges, best practices, and design decisions},
pages = {1--20},
month = {September},
year = {2023},
journal = {Journal of Systems and Software},
url = {http://www.ipr.mdu.se/publications/6777-}
}