Secure Cloud Native Healthcare Platforms with AI DevOps Machine Learning ETL Workloads and Automation
DOI:
https://doi.org/10.15662/IJEETR.2023.0504004Keywords:
Cloud-native healthcare, AI DevOps, machine learning systems, ETL workloads, healthcare automation, data security, CI/CD pipelines, scalable analytics, privacy preservation, microservices architecture, intelligent monitoring, digital health platformsAbstract
Secure cloud-native healthcare platforms are increasingly essential for managing sensitive clinical data, supporting large-scale analytics, and enabling intelligent automation across distributed environments. This study presents an integrated framework that combines artificial intelligence, DevOps practices, and machine learning pipelines to support secure, scalable, and resilient healthcare systems. The proposed architecture leverages cloud-native principles such as containerization, microservices, and CI/CD automation to streamline ETL workloads, accelerate model deployment, and ensure continuous system reliability. Machine learning models are embedded within automated data pipelines to enable real-time clinical insights, predictive analytics, and operational optimization, while security-by-design principles address data privacy, regulatory compliance, and cyber resilience. The framework emphasizes automated testing, monitoring, and governance to reduce operational risks and improve system transparency. By unifying AI-driven analytics with DevOps automation and secure cloud infrastructure, this approach supports next-generation healthcare platforms capable of handling complex data workflows, evolving threat landscapes, and dynamic clinical demands
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