Solution Patterns for Machine Learning

Despite the hype around machine learning (ML), many organizations are struggling to derive business value from ML capabilities. Design patterns have long been used in software engineering to enhance design effectiveness and to speed up the development process. The contribution of this paper is two-f...

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Bibliographic Details
Published inAdvanced Information Systems Engineering pp. 627 - 642
Main Authors Nalchigar, Soroosh, Yu, Eric, Obeidi, Yazan, Carbajales, Sebastian, Green, John, Chan, Allen
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Advanced analytics
Business analytics
Conceptual modeling
Design patterns
Machine learning
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Summary:Despite the hype around machine learning (ML), many organizations are struggling to derive business value from ML capabilities. Design patterns have long been used in software engineering to enhance design effectiveness and to speed up the development process. The contribution of this paper is two-fold. First, it introduces solution patterns as an explicit way of representing generic and well-proven ML designs for commonly-known and recurring business analytics problems. Second, it reports on the feasibility, expressiveness, and usefulness of solution patterns for ML, in collaboration with an industry partner. It provides a prototype architecture for supporting the use of solution patterns in real world scenarios. It presents a proof-of-concept implementation of the architecture and illustrates its feasibility. Findings from the collaboration suggest that solution patterns can have a positive impact on ML design and development efforts.
ISBN:9783030212896
3030212890
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-030-21290-2_39