Related papers: A Tale of Two Systems: Characterizing Architectural Complexity on Machine Learning-Enabled Systems

A Tale of Two Systems: Characterizing Architectural Complexity on Machine Learning-Enabled Systems

URL: http://arxiv.org/abs/2506.11295v1
Date: Thu, 12 Jun 2025 20:54:28 GMT
Title: A Tale of Two Systems: Characterizing Architectural Complexity on Machine Learning-Enabled Systems
Authors: Renato Cordeiro Ferreira,
Abstract summary: This research aims to introduce a metrics-based architectural model to characterize the complexity of ML-Enabled Systems.<n>The goal is to support architectural decisions, providing a guideline for the inception and growth of these systems.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

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