用端口与适配器模式复用代码，构建可扩展的海上异常检测系统。
Reusability in MLOps: Leveraging Ports and Adapters to Build a Microservices Architecture for the Maritime Domain
- 基于端口与适配器模式，从同一代码库构建多个微服务。
- 解决多服务间复用难题，提升开发效率与系统可维护性。
- 适合想提升MLOps架构灵活性的工程与数据团队。
机器学习驱动系统（MLES）因需多个组件协同完成业务目标而具有固有复杂性。本文报告了在构建面向海事领域的异常检测系统Ocean Guard过程中，应用软件架构复用技术的经验。重点阐述了如何利用端口与适配器模式，从单一代码库支持多个微服务的构建，同时揭示了面临的挑战与关键教训。该实践旨在激励软件工程师、机器学习工程师及数据科学家采用六边形架构模式，以构建更可维护、可复用的MLES。
原文摘要 · Abstract (English)
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.
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