Challenges in Deploying Machine Learning: A Survey of Case Studies
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Publication Date
2023Journal Title
ACM Computing Surveys
ISSN
0360-0300
Publisher
Association for Computing Machinery (ACM)
Type
Article
This Version
AM
Metadata
Show full item recordCitation
Paleyes, A., Urma, R., & Lawrence, N. (2023). Challenges in Deploying Machine Learning: A Survey of Case Studies. ACM Computing Surveys https://doi.org/10.1145/3533378
Abstract
<jats:p>In recent years, machine learning has transitioned from a field of academic research interest to a field capable of solving real-world business problems. However, the deployment of machine learning models in production systems can present a number of issues and concerns. This survey reviews published reports of deploying machine learning solutions in a variety of use cases, industries, and applications and extracts practical considerations corresponding to stages of the machine learning deployment workflow. By mapping found challenges to the steps of the machine learning deployment workflow, we show that practitioners face issues at each stage of the deployment process. The goal of this article is to lay out a research agenda to explore approaches addressing these challenges.</jats:p>
Keywords
cs.LG, cs.LG
Sponsorship
Senior AI Fellowship received by Prof. Neil D. Lawrence from UKRI through Alan Turing Institute
Identifiers
External DOI: https://doi.org/10.1145/3533378
This record's URL: https://www.repository.cam.ac.uk/handle/1810/336516
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