Microservice Architecture Patterns for Scalable Machine Learning Systems

TL;DR

This paper reviews how microservice architectures are used to build scalable, efficient, and responsive machine learning systems, highlighting industry practices and simulation results that demonstrate improved performance.

Contribution

It provides a comprehensive review of microservice patterns in ML systems, including industry case studies and simulation evidence of performance benefits.

Findings

Microservice architectures reduce latency in ML systems.

Microservices improve scalability and responsiveness.

Industry case studies demonstrate successful deployment.

Abstract

Machine learning is now a central part of how modern systems are built and used, powering everything from personalized recommendations to large-scale business analytics. As its role grows, organizations are facing new challenges in managing, deploying, and scaling these models efficiently. One approach that has gained wide adoption is the use of microservice architectures, which break complex machine learning systems into smaller, independent parts that can be built, updated, and scaled on their own. In this paper, we review how major companies such as Netflix, Uber, and Google use microservices to handle key machine learning tasks like training, deployment, and monitoring. We discuss the main challenges involved in designing such systems and explore how microservices fit into large-scale applications, particularly in recommendation systems. We also present some simulation studies showing that microservice-based designs can reduce latency and improve scalability, leading to faster, more efficient, and more responsive machine learning applications in real-world and large-scale systems.