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
References
1. Kamadi, S. (2022). Adaptive Federated Data Science & MLOps Architecture: A Comprehensive Framework for Distributed Machine Learning Systems. International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), 8(6), 745-755.
2. Kesavan, E. (2022). An empirical research in software testing in fuzzy TOPICS method. REST Journal on Data Analytics and Artificial Intelligence, 1(3), 51–56. https://doi.org/10.46632/jdaai/1/3/7
3. Mohana, P., Muthuvinayagam, M., Umasankar, P., & Muthumanickam, T. (2022, March). Automation using Artificial intelligence based Natural Language processing. In 2022 6th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 1735-1739). IEEE.
4. Ramidi, M. (2022). Developing resilient offline-first architectures for mobile health and clinical research applications. International Journal of Computer Technology and Electronics Communication (IJCTEC), 5(1), 4518–4529.
5. Lokiny, N. (2019). Comparative Study of Cloud Providers (AWS, Azure, Google Cloud) using Artificial Intelligence with DevOps. International Journal of Science and Research (IJSR), 8(8), 2326-2329.
6. Sudha, N., Kumar, S. S., Rengarajan, A., & Rao, K. B. (2021). Scrum Based Scaling Using Agile Method to Test Software Projects Using Artificial Neural Networks for Block Chain. Annals of the Romanian Society for Cell Biology, 25(4), 3711-3727.
7. Genne, S. (2022). Designing accessibility-first enterprise web platforms at scale. International Journal of Research and Applied Innovations (IJRAI), 5(5), 7679–7690.
8. Panda, M. R., & Sethuraman, S. (2022). Blockchain-Based Regulatory Reporting with Zero-Knowledge Proofs. Essex Journal of AI Ethics and Responsible Innovation, 2, 495-532.
9. Vaidya, S., Shah, N., Shah, N., & Shankarmani, R. (2020, May). Real-time object detection for visually challenged people. In 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 311-316). IEEE.
10. Navandar, P. (2022). SMART: Security Model Adversarial Risk-based Tool. International Journal of Research and Applied Innovations, 5(2), 6741-6752.
11. Anand, L., & Neelanarayanan, V. (2019). Feature Selection for Liver Disease using Particle Swarm Optimization Algorithm. International Journal of Recent Technology and Engineering (IJRTE), 8(3), 6434-6439.
12. Surisetty, L. S. (2022). Modernizing Legacy Systems with AI Orchestration: From Monoliths to Autonomous Micro services. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 5(6), 7299-7306.
13. Devi, C., Vunnam, N., & Jeyaraman, J. (2022). HyperLogLog-Based Compliance Coverage Estimation for Distributed Datasets. Essex Journal of AI Ethics and Responsible Innovation, 2, 495-530.
14. Sriramoju, S. (2022). API-driven account onboarding framework with real-time compliance automation. International Journal of Research and Applied Innovations (IJRAI), 5(6), 8132–8144.
15. Muthusamy, P., Keezhadath, A. A., & Burila, R. K. (2022). Performance Optimization in Large-Scale ETL Workloads: Advanced Techniques in Distributed Computing. Los Angeles Journal of Intelligent Systems and Pattern Recognition, 2, 113-147.
16. Chivukula, V. (2020). IMPACT OF MATCH RATES ON COST BASIS METRICS IN PRIVACY-PRESERVING DIGITAL ADVERTISING. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 3(4), 3400-3405.
17. Gangina, P. (2022). Resilience engineering principles for distributed cloud-native applications under chaos. International Journal of Computer Technology and Electronics Communication, 5(5), 5760–5770.
18. Chennamsetty, C. S. (2022). Hardware-Software Co-Design for Sparse and Long-Context AI Models: Architectural Strategies and Platforms. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 5(5), 7121-7133.
19. Adari, V. K. (2021). Building trust in AI-first banking: Ethical models, explainability, and responsible governance. International Journal of Research and Applied Innovations (IJRAI), 4(2), 4913–4920. https://doi.org/10.15662/IJRAI.2021.0402004
20. Mogil, V. B. (2023). Implementing role-based access control for healthcare data using SharePoint. International Journal of Engineering & Extended Technologies Research, 5(2), 6323–6333.
21. Singh, A. (2020). Impact of network topology changes on performance. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 3(4), 3687–3692. https://doi.org/10.15662/IJRPETM.2020.0304003
22. Nagarajan, C., Umadevi, K., Saravanan, S., & Muruganandam, M. (2022). Performance investigation of ANFIS and PSO DFFP based boost converter with NICI using solar panel. International Journal of Engineering, Science and Technology, 14(2), 11-21.
23. Sreekala, K., Rajkumar, N., Sugumar, R., Sagar, K. D., Shobarani, R., Krishnamoorthy, K. P., ... & Yeshitla, A. (2022). Skin diseases classification using hybrid AI based localization approach. Computational Intelligence and Neuroscience, 2022(1), 6138490.
24. Ponugoti, M. (2022). Integrating API-first architecture with experience-centric design for seamless insurance platform modernization. International Journal of Humanities and Information Technology (IJHIT), 4(1–3), 117–136.
25. Anumula, S. R. (2022). Transparent and auditable decision-making in enterprise platforms. International Journal of Research and Applied Innovations (IJRAI), 5(5), 7691–7702. https://doi.org/10.15662/IJRAI.2022.0505007
26. Mudunuri, P. R. (2022). Engineering audit-ready CI/CD pipelines for federally regulated scientific computing. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(5), 5342–5351.
27. Vimal Raja, G. (2022). Leveraging Machine Learning for Real-Time Short-Term Snowfall Forecasting Using MultiSource Atmospheric and Terrain Data Integration. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 5(8), 1336-1339.
28. Gaddapuri, N. S. (2023). A COMPARATIVE STUDY OF HEALTHCARE SYSTEMS IN THE UNITED STATES AND INDIA. Power System Protection and Control, 51(2), 18-31.
29. Pandey, A., Chauhan, A., & Gupta, A. (2023). Voice Based Sign Language Detection For Dumb People Communication Using Machine Learning. Journal of Pharmaceutical Negative Results, 14(2)
