A Tale of Two Systems: Characterizing Architectural Complexity on Machine Learning-Enabled Systems
Not on the map. Why?
Abstract (arXiv)
How can the complexity of ML-enabled systems be managed effectively? The goal of this research is to investigate how complexity affects ML-Enabled Systems (MLES). To address this question, this research aims to introduce a metrics-based architectural model to characterize the complexity of MLES. The goal is to support architectural decisions, providing a guideline for the inception and growth of these systems. This paper brings, side-by-side, the architecture representation of two systems that can be used as case studies for creating the metrics-based architectural model: the SPIRA and the Ocean Guard MLES.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 18 references that are in the map. First posted on arXiv 12 Jun 2025.
Builds on
6 of 18 references.
References · 6
- 2020A Software Engineering Perspective on Engineering Machine Learning Systems: State of the Art and Challengescited by 33
- 2022Operationalizing Machine Learning: An Interview Studycited by 25
- 2023The Product Beyond the Model -- An Empirical Study of Repositories of Open-Source ML Productscited by 10
- 2025MLOps with Microservices: A Case Study on the Maritime Domaincited by 2
- 2025A Metrics-Oriented Architectural Model to Characterize Complexity on Machine Learning-Enabled Systemscited by 2
- 2025Making a Pipeline Production-Ready: Challenges and Lessons Learned in the Healthcare Domaincited by 1
Built on by
Not cited yet.