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

TL;DR

This paper investigates how to measure and manage the architectural complexity of ML-enabled systems using a metrics-based model, supported by case studies of two systems, SPIRA and Ocean Guard.

Contribution

It introduces a novel metrics-based architectural model specifically designed for ML-enabled systems, aiding architectural decision-making.

Findings

Comparison of two ML-enabled systems, SPIRA and Ocean Guard.

Development of a metrics-based architectural characterization approach.

Guidelines for managing complexity in ML-enabled system architecture.

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.