提出一套指标驱动的架构模型，量化评估机器学习系统复杂度。
A Metrics-Oriented Architectural Model to Characterize Complexity on Machine Learning-Enabled Systems
- 基于参考架构扩展，采集机器学习系统的运行指标
- 构建可量化的复杂度评估框架，支持系统设计决策
- 适合关注系统可维护性与演进规划的研发团队
如何有效管理机器学习增强型系统（MLES）的复杂性？本研究旨在探究复杂性对MLES的影响，并提出一种基于指标的架构模型来表征其复杂度。该模型旨在支持架构决策，为系统的初始设计与持续演化提供指导。本文展示了构建该指标化架构模型的第一步：在现有参考架构基础上进行扩展，以捕捉MLES的相关指标。
原文摘要 · Abstract (English)
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 showcases the first step for creating the metrics-based architectural model: an extension of a reference architecture that can describe MLES to collect their metrics.
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