MLOps with Microservices: A Case Study on the Maritime Domain

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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.
Original languageEnglish
Title of host publicationService-Oriented Computing
Subtitle of host publication19th Symposium and Summer School, SummerSOC 2025, Crete, Greece, June 16–21, 2025, Revised Selected Papers
Pages3-15
Volume2602
ISBN (Electronic)978-3-032-07313-6
DOIs
Publication statusPublished - 16 Jun 2025

Publication series

NameCommunications in Computer and Information Science
PublisherSpringer Nature
Volume2602
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Keywords

  • Microservices
  • MLOps
  • Software Architecture
  • Machine Learning Enabled Systems
  • Maritime Domain
  • Case Study

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