Enabling Machine Learning in Software Architecture Frameworks

Armin Moin, Atta Badii, Stephan Gunnemann, Moharram Challenger

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

Abstract

Several architecture frameworks for software, systems, and enterprises have been proposed in the literature. They have identified various stakeholders and defined architecture viewpoints and views to frame and address stakeholder concerns. However, the Machine Learning (ML) and data science-related concerns of data scientists and data engineers are yet to be included in existing architecture frameworks. We interviewed 65 experts from around 25 organizations in over ten countries to devise and validate the proposed framework that addresses the mentioned shortcoming.

OriginalspracheEnglisch
TitelProceedings - 2023 IEEE/ACM 2nd International Conference on AI Engineering - Software Engineering for AI, CAIN 2023
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
Seiten92-93
Seitenumfang2
ISBN (elektronisch)9798350301137
DOIs
PublikationsstatusVeröffentlicht - 2023
Veranstaltung2nd IEEE/ACM International Conference on AI Engineering - Software Engineering for AI, CAIN 2023 - Melbourne, Australien
Dauer: 15 Mai 202316 Mai 2023

Publikationsreihe

NameProceedings - 2023 IEEE/ACM 2nd International Conference on AI Engineering - Software Engineering for AI, CAIN 2023

Konferenz

Konferenz2nd IEEE/ACM International Conference on AI Engineering - Software Engineering for AI, CAIN 2023
Land/GebietAustralien
OrtMelbourne
Zeitraum15/05/2316/05/23

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