Solution Patterns for Machine Learning

  • Soroosh NalchigarEmail author
  • Eric Yu
  • Yazan Obeidi
  • Sebastian Carbajales
  • John Green
  • Allen Chan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11483)


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.


Conceptual modeling Machine learning Advanced analytics Business analytics Design patterns 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Soroosh Nalchigar
    • 1
    Email author
  • Eric Yu
    • 1
  • Yazan Obeidi
    • 2
  • Sebastian Carbajales
    • 2
  • John Green
    • 2
  • Allen Chan
    • 2
  1. 1.Department of Computer ScienceUniversity of TorontoTorontoCanada
  2. 2.IBM Canada Ltd.MarkhamCanada

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