Architectural Patterns for ML in Microservices & Cloud Architecture

Authors

Santhosh Podduturi

Abstract

Machine Learning (ML) is revolutionizing industries by enabling intelligent decision-making and automation. However, deploying ML models in modern cloud-native applications requires scalable, maintainable, and efficient architectural patterns. This paper explores architectural patterns that facilitate the seamless integration of ML into microservices and cloud-based ecosystems. It discusses various deployment models, including ML Model as a Service (MaaS), Event-Driven ML, Federated Learning, and Serverless ML, highlighting their advantages, challenges, and best practices.
The paper delves into key considerations such as model scalability, versioning, security, real-time inference, and model drift management in microservices architectures. Analyze how cloud-native technologies, such as Kubernetes, serverless computing, and API gateways, can enhance the deployment and lifecycle management of ML models. Through this study, the aim is to provide software architects, ML engineers, and cloud practitioners with practical insights and strategies to design robust, scalable, and maintainable ML-driven microservices in cloud environments.

Keywords

Microservices Architecture, Artificial Intelligence (AI), Machine Learning (ML), Scalability, Automation.

Architectural Patterns for ML in Microservices & Cloud Architecture. Santhosh Podduturi. 2023. IJIRCT, Volume 9, Issue 1. Pages 1-13. https://www.ijirct.org/viewPaper.php?paperId=2503083