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

arXiv: https://arxiv.org/abs/2506.11295 · HTML: https://mlsanity.com/paper/2506.11295/

## 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 (most cited first, 6 of 6)

- [A Software Engineering Perspective on Engineering Machine Learning Systems: State of the Art and Challenges](https://mlsanity.com/paper/2012.07919/) (2020)
- [Operationalizing Machine Learning: An Interview Study](https://mlsanity.com/paper/2209.09125/) (2022)
- [The Product Beyond the Model -- An Empirical Study of Repositories of Open-Source ML Products](https://mlsanity.com/paper/2308.04328/) (2023)
- [MLOps with Microservices: A Case Study on the Maritime Domain](https://mlsanity.com/paper/2506.06202/) (2025)
- [A Metrics-Oriented Architectural Model to Characterize Complexity on Machine Learning-Enabled Systems](https://mlsanity.com/paper/2506.08153/) (2025)
- [Making a Pipeline Production-Ready: Challenges and Lessons Learned in the Healthcare Domain](https://mlsanity.com/paper/2506.06946/) (2025)

## 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.