MLOps with Microservices: A Case Study on the Maritime Domain

TL;DR

This paper presents a case study on implementing MLOps in the maritime domain using microservices architecture, highlighting challenges, lessons learned, and contract-based design for building scalable, collaborative ML-enabled systems.

Contribution

It demonstrates how microservices and contract-based design can be effectively applied to MLOps in a real-world maritime anomaly detection system.

Findings

Microservices enable parallel development by multiple teams.

Contract-based design improves system reliability and collaboration.

The approach can be adapted to other domains with similar needs.

Abstract

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.