AI Driven DevOps and Machine Learning Systems for Privacy Preserving Healthcare and Digital Advertising

Marta Kwiatkowska

doi:10.15662/IJRAI.2023.0603009

Authors

Marta Kwiatkowska Senior Data Engineer, Sweden Author

DOI:

https://doi.org/10.15662/IJRAI.2023.0603009

Keywords:

AI Driven DevOps, Machine Learning Systems, Privacy Preserving Analytics, Healthcare Data Security, Digital Advertising Platforms, Cloud Native Architecture, CI CD Pipelines, Automated ETL Workloads, API First Microservices, Enterprise Data Governance, Secure Data Integration, Continuous Monitoring

Abstract

AI-driven DevOps and machine learning systems are increasingly transforming privacy-preserving healthcare and digital advertising platforms by enabling scalable intelligence, automation, and secure data utilization. This paper proposes an integrated cloud-native architecture that combines machine learning pipelines, continuous integration and continuous delivery DevOps practices, and privacy-aware data engineering to support sensitive healthcare analytics and compliant digital advertising workflows. The framework leverages distributed datasets, automated ETL pipelines, and API-first microservices to enable real-time model training, deployment, and monitoring across heterogeneous environments.

Privacy preservation is enforced through secure data governance mechanisms, encryption-aware pipelines, and policy-driven access controls, ensuring compliance with healthcare and data protection regulations. AI-enabled DevOps workflows improve model reliability, accelerate experimentation, and enhance operational resilience through automated testing and continuous security validation. The proposed system is designed for enterprise-scale deployment and interoperability with modern cloud platforms and digital ecosystems, including regulated healthcare infrastructures and advertising technology stacks. By unifying machine learning systems with DevOps automation and privacy-by-design principles, the architecture delivers trustworthy analytics, reduced operational risk, and sustainable innovation across data-intensive domains.

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