# A Metrics-Oriented Architectural Model to Characterize Complexity on Machine Learning-Enabled Systems

arXiv: https://arxiv.org/abs/2506.08153 · HTML: https://mlsanity.com/paper/2506.08153/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 29 references that are in the map.
- First posted on arXiv 9 Jun 2025.

## Builds on (most cited first, 3 of 3)

- [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)
- [Data Pipeline Quality: Influencing Factors, Root Causes of Data-related Issues, and Processing Problem Areas for Developers](https://mlsanity.com/paper/2309.07067/) (2023)

## Built on by (most cited first, 2 of 2)

- [A Tale of Two Systems: Characterizing Architectural Complexity on Machine Learning-Enabled Systems](https://mlsanity.com/paper/2506.11295/) (2025)
- [Reusability in MLOps: Leveraging Ports and Adapters to Build a Microservices Architecture for the Maritime Domain](https://mlsanity.com/paper/2512.08657/) (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 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.