用微服务架构构建海事异常检测系统，提升团队协作效率
MLOps with Microservices: A Case Study on the Maritime Domain
- 采用微服务架构支持多团队并行开发
- 通过代码/模型/数据契约实现服务间规范对接
- 为机器学习工程化提供可复用的实践参考
本案例研究描述了在海事领域构建机器学习增强系统（MLES）Ocean Guard时面临的挑战与经验。该系统旨在实现海事异常检测，采用微服务架构设计，使多个团队能够并行开发。研究提出通过契约驱动的方式实现MLOps目标：使用代码、模型和数据契约建立各服务间的规范。这些契约明确了接口标准，保障了系统的可维护性与协作效率。本案例旨在启发软件工程师、机器学习工程师与数据科学家借鉴类似方法构建高效、可扩展的ML系统。
原文摘要 · Abstract (English)
This case study describes challenges and lessons learned on building Ocean Guard: a Machine Learning-Enabled System (MLES) for anomaly detection in the maritime domain. First, the paper presents the system's specification, and architecture. Ocean Guard was designed with a microservices' architecture to enable multiple teams to work on the project in parallel. Then, the paper discusses how the developers adapted contract-based design to MLOps for achieving that goal. As a MLES, Ocean Guard employs code, model, and data contracts to establish guidelines between its services. This case study hopes to inspire software engineers, machine learning engineers, and data scientists to leverage similar approaches for their systems.
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