通过两个系统对比，构建衡量机器学习系统复杂性的评估模型。
A Tale of Two Systems: Characterizing Architectural Complexity on Machine Learning-Enabled Systems
- 基于SPIRA与Ocean Guard系统，建立架构复杂性度量模型。
- 提出可量化系统演进过程的复杂性指标体系。
- 适合系统架构师在设计和扩展ML系统时参考。
如何有效管理机器学习增强系统（MLES）的复杂性？本研究旨在探究复杂性对MLES的影响，并提出一种基于度量的架构模型来表征其复杂性，以支持架构决策，为系统的初始设计与持续演进提供指导。本文通过对比分析两个典型案例系统——SPIRA与Ocean Guard MLES——的架构表示，构建了该度量模型的基础框架。
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 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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