TY - CHAP
T1 - MLOps with Microservices
T2 - A Case Study on the Maritime Domain
AU - Ferreira, Renato Cordeiro
AU - Trapmann, Rowanne
AU - van den Heuvel, Willem-Jan
PY - 2025/6/16
Y1 - 2025/6/16
N2 - 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.
AB - 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.
KW - Microservices
KW - MLOps
KW - Software Architecture
KW - Machine Learning Enabled Systems
KW - Maritime Domain
KW - Case Study
UR - https://doi.org/10.1007/978-3-032-07313-6_1
U2 - 10.1007/978-3-032-07313-6_1
DO - 10.1007/978-3-032-07313-6_1
M3 - Chapter
SN - 978-3-032-07312-9
VL - 2602
T3 - Communications in Computer and Information Science
SP - 3
EP - 15
BT - Service-Oriented Computing
ER -