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Pattern Language for Designing Distributed AI Systems

In: City, Society, and Digital Transformation

Author

Listed:
  • Satish Mahadevan Srinivasan

    (Penn State Great Valley)

  • Shahed Mahbub

    (Penn State Great Valley)

  • Raghvinder S. Sangwan

    (Penn State Great Valley)

  • Youakim Badr

    (Penn State Great Valley)

  • Partha Mukherjee

    (Penn State Great Valley)

Abstract

Design of Artificial Intelligence (AI) and Machine Learning (ML) applications, hereafter referred to as AI systems, is often based on a typical ML pipeline. One of the reasons for choosing this approach is its simplicity and modularity. While simple, such an approach tends to be rigid with respect to changing needs, technologies, devices, and algorithms. Recent research on design patterns for ML has introduced best practices for engineering AI systems. We examine a set of these patterns, or a pattern language, where individually selected patterns can build on each other to offer a complete design solution for a distributed AI system. We demonstrate the use of this pattern language to design an AI system for emotion classification of social media content. The result is an AI system that is not only easy to change and reuse in a similar context, for instance emotion classification of image data, but one whose architecture has better performance, usability, maintainability, security, and reliability.

Suggested Citation

  • Satish Mahadevan Srinivasan & Shahed Mahbub & Raghvinder S. Sangwan & Youakim Badr & Partha Mukherjee, 2022. "Pattern Language for Designing Distributed AI Systems," Lecture Notes in Operations Research, in: Robin Qiu & Wai Kin Victor Chan & Weiwei Chen & Youakim Badr & Canrong Zhang (ed.), City, Society, and Digital Transformation, chapter 0, pages 467-477, Springer.
    • Handle: RePEc:spr:lnopch:978-3-031-15644-1_34
    DOI: 10.1007/978-3-031-15644-1_34
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