Reusability in MLOps: Leveraging Ports and Adapters to Build a Microservices Architecture for the Maritime Domain
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Abstract (arXiv)
ML-Enabled Systems (MLES) are inherently complex since they require multiple components to achieve their business goal. This experience report showcases the software architecture reusability techniques applied while building Ocean Guard, an MLES for anomaly detection in the maritime domain. In particular, it highlights the challenges and lessons learned to reuse the Ports and Adapters pattern to support building multiple microservices from a single codebase. This experience report hopes to inspire software engineers, machine learning engineers, and data scientists to apply the Hexagonal Architecture pattern to build their MLES.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 14 references that are in the map. First posted on arXiv 9 Dec 2025.
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4 of 14 references.
References · 4
- 2022Machine Learning Operations (MLOps): Overview, Definition, and Architecturecited by 128
- 2017Re-run, Repeat, Reproduce, Reuse, Replicate: Transforming Code into Scientific Contributionscited by 3
- 2025MLOps with Microservices: A Case Study on the Maritime Domaincited by 2
- 2025A Metrics-Oriented Architectural Model to Characterize Complexity on Machine Learning-Enabled Systemscited by 2
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