SECURE AND INTELLIGENT MICROSERVICES ARCHITECTURE FOR MACHINE LEARNING APPLICATIONS USING ARTIFICIAL INTELLIGENCE

Armand Alberto Gil  Journal

SECURE AND INTELLIGENT MICROSERVICES ARCHITECTURE FOR MACHINE LEARNING APPLICATIONS USING ARTIFICIAL INTELLIGENCE

Authors

Armand Alberto Gil Journal

Principal AI & Cloud Systems Architect, France.

Keywords:

Microservices, Artificial Intelligence, Machine Learning, Security Architecture, Intelligent Orchestration, AI Ops.

Synopsis

Purpose – This paper explores the design and implementation of a secure and intelligent microservices-based architecture for machine learning (ML) applications using artificial intelligence (AI) techniques.

Design/methodology/approach – We propose a modular microservices architecture enhanced by AI-driven components such as autonomous security agents, smart load balancers, and dynamic service orchestration. The architecture is evaluated in terms of scalability, security, and ML model deployment efficiency.

Findings – Results indicate that AI-enhanced microservices enable faster model training, improved anomaly detection, and dynamic threat mitigation. The architecture demonstrates robustness under variable network loads and supports real-time model retraining.

Practical implications – The proposed architecture aids industries in deploying secure, scalable ML systems—crucial in finance, healthcare, and autonomous systems. Integrating AI at the system level strengthens cybersecurity and enhances operational efficiency.

Originality/value – This study is among the first to integrate intelligent security mechanisms with a microservices architecture tailored for ML operations, promoting a new standard for resilient AI-driven infrastructure.

References

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