Scalable ML Deployment on OCI with Network Intelligence and Risk-Aware Software Engineering for Healthcare and Banking Systems
DOI:
https://doi.org/10.15662/IJARCST.2025.0806808Keywords:
Oracle Cloud Infrastructure, Scalable ML Deployment, Network Intelligence, Risk-Aware Software Engineering, Healthcare Analytics, Banking Systems, Cloud-Native ArchitectureAbstract
The increasing adoption of artificial intelligence across healthcare and banking demands scalable, secure, and high-performance deployment architectures. This paper presents a comprehensive framework for Scalable ML Deployment on Oracle Cloud Infrastructure (OCI), integrating network intelligence and risk-aware software engineering principles to support mission-critical analytics workloads. Leveraging OCI’s high-performance compute, distributed networking, and autonomous data services, the proposed architecture enables efficient training, deployment, and orchestration of machine learning models at scale. In healthcare, the framework supports predictive diagnostics, real-time patient monitoring, and operational optimization, while in banking it enhances fraud detection, credit risk assessment, and customer behavior analytics. Network intelligence techniques—including adaptive routing, bandwidth optimization, and latency-aware workloads—ensure reliable and continuous model operation across distributed environments. The incorporation of risk-aware software engineering strengthens system resilience through secure design patterns, threat modeling, and compliance-driven development practices. Overall, this study demonstrates how OCI can enable scalable, intelligent, and risk-optimized ML ecosystems capable of driving digital transformation in both healthcare and banking sectors.
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