2021/03/14 by Henry Muccini, Muccini, Henry, Karthik Vaidhyanathan +1 · 1 citation
Computer Science · Decision Sciences · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Scientific Computing and Data Management #Software Engineering (cs.SE) #Software Engineering Techniques and Practices #Software System Performance and Reliability
paper · pdf · doi:10.48550/arxiv.2103.07950
openalex publication_date 2021/03/14 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
The increasing usage of machine learning (ML) coupled with the software\narchitectural challenges of the modern era has resulted in two broad research\nareas: i) software architecture for ML-based systems, which focuses on\ndeveloping architectural techniques for better developing ML-based software\nsystems, and ii) ML for software architectures, which focuses on developing ML\ntechniques to better architect traditional software systems. In this work, we\nfocus on the former side of the spectrum with a goal to highlight the different\narchitecting practices that exist in the current scenario for architecting\nML-based software systems. We identify four key areas of software architecture\nthat need the attention of both the ML and software practitioners to better\ndefine a standard set of practices for architecting ML-based software systems.\nWe base these areas in light of our experience in architecting an ML-based\nsoftware system for solving queuing challenges in one of the largest museums in\nItaly.\n