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To overcome them, engineers often use specific architectural patterns; however, their impact on ML systems is difficult to quantify. While software architecture\u2019s impact on traditional systems is well understood, more research is needed for ML systems. This study proposes a framework for quantitative assessment of architectural patterns in ML systems, focusing on scalability and performance metrics for cost-effective CPU-based inference. We integrate these metrics into a systematic evaluation process for the selection of architectural patterns and demonstrate their application through a case study. The approach shown in the paper should enable software architects to objectively analyze and select optimal patterns, addressing key challenges in ML system design.<\/jats:p>","DOI":"10.1142\/s0218194025500639","type":"journal-article","created":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T04:32:40Z","timestamp":1758947560000},"page":"1059-1088","source":"Crossref","is-referenced-by-count":0,"title":["A Quantitative Framework for Evaluating Architectural Patterns in ML Systems"],"prefix":"10.1142","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2295-4513","authenticated-orcid":false,"given":"Simeon","family":"Emanuilov","sequence":"first","affiliation":[{"name":"Department of Software Technologies, Faculty of Mathematics and Informatics, Sofia University \u201cSt. Kliment Ohridski\u201d, 5 James Bourchier Blvd., 1164 Sofia, Bulgaria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6197-1212","authenticated-orcid":false,"given":"Aleksandar","family":"Dimov","sequence":"additional","affiliation":[{"name":"Department of Software Technologies, Faculty of Mathematics and Informatics, Sofia University \u201cSt. Kliment Ohridski\u201d, 5 James Bourchier Blvd., 1164 Sofia, Bulgaria"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2026,3,19]]},"reference":[{"key":"S0218194025500639BIB001","unstructured":"Institute for Human-Centered AI, The AI Index 2024 Annual Report, Stanford University, Palo Alto (2024)."},{"key":"S0218194025500639BIB002","volume-title":"Designing Machine Learning Systems","author":"Huyen C.","year":"2022"},{"key":"S0218194025500639BIB003","unstructured":"C. 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