Machine Learning-Enhanced Backend Systems: Scalable Architectures, Automated Model Deployment, and Infrastructure Strategies for Investor Ready AI Platforms

Authors

  • Prakash Wagle , Lingling Tan , Jay Mehta

Keywords:

Artificial Intelligence, Sustainable Healthcare, Machine Learning, Workflow Automation, Backend Systems, Structural Equation Modeling, Clinical Efficiency.

Abstract

This study explores the transformative potential of AI-driven innovation in promoting asustainable future within the healthcare sector. By integrating machine learning technologiesinto backend systems, the research investigates improvements in clinical efficiency, patientoutcomes, and environmental sustainability

References

Abdullah, M., Iqbal, W., & Erradi, A. (2019). Unsupervised learning approach for web application auto-decomposition into microservices. Journal of Systems and Software, 151, 243-257.

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Published

2025-05-29

How to Cite

Prakash Wagle , Lingling Tan , Jay Mehta. (2025). Machine Learning-Enhanced Backend Systems: Scalable Architectures, Automated Model Deployment, and Infrastructure Strategies for Investor Ready AI Platforms. Journal of Computational Analysis and Applications (JoCAAA), 34(5), 225–238. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2884

Issue

Vol. 34 No. 5 (2025): JOCAAA

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.