An End-to-End Microservices Architecture for Continuous Training, Deployment, and Monitoring of AI Models

J. Linz Colell

An End-to-End Microservices Architecture for Continuous Training, Deployment, and Monitoring of AI Models

Authors

J. Linz Colell

MLOps Engineer – Microservices & AI Deployment , Spain

Keywords:

Microservices, MLOps, Model Deployment, Continuous Training, Model Monitoring, AI Infrastructure, Devops, CI/CD

Synopsis

Purpose: This paper proposes a complete microservices-based architecture for the continuous integration, delivery, and monitoring of AI models, enabling faster iterations, modular scalability, and model lifecycle observability.

Design/methodology/approach: We design a layered, containerized system composed of services responsible for data preprocessing, model training, deployment, versioning, A/B testing, and monitoring. Orchestration is managed via Kubernetes and CI/CD pipelines to streamline workflows.

Findings: Microservices-based designs significantly improve deployment flexibility, fault isolation, and model update frequency. By coupling observability tools with model drift detectors, the architecture enables reactive retraining and performance visibility.

Practical implications: Enterprises adopting this framework can seamlessly retrain models on new data, reduce downtimes during deployment, and gain actionable insights via integrated monitoring and alerts.

Originality/value: This paper unifies fragmented practices from MLOps and software engineering into a scalable, modular blueprint tailored for production-grade AI systems.

References

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