Scalable Orchestration of AI Model Lifecycles in Multi-Zone Cloud Platformsthrough Intelligent Resource Prediction and Auto-DeploymentPipelines
Keywords:
AI lifecycle, orchestration, multi-zone cloud, auto-deployment, resource prediction, cloud-native ML, DevOps AIAbstract
Efficiently managing the AI model lifecycle in cloud-native ecosystems has become increasingly complex, especially in multi-zone cloud platforms. To maintain performance and reliability across geographies, intelligent orchestration strategies are necessary to automate deployment, predict resource needs, and ensure service continuity. This paper presents a scalable framework that utilizes AI-driven resource prediction and continuous deployment pipelines to manage the end-to-end model lifecycle, from training to retirement. The framework emphasizes cross-zone synchronization, proactive scaling, and minimal human intervention. Through comparative studies and architectural analysis, the proposed approach demonstrates improved latency, cost-efficiency, and fault tolerance.
References
Sculley, D., et al. (2015). Hidden technical debt in machine learning systems. NeurIPS.
Zaharia, M., et al. (2018). MLflow: A platform for the ML lifecycle. KDD.
Adapa, C.S.R. (2025). Building a standout portfolio in master data management (MDM) and data engineering. International Research Journal of Modernization in En-gineering Technology and Science, 7(3), 8082–8099. https://doi.org/10.56726/IRJMETS70424
Baylor, D., et al. (2017). TensorFlow Serving: Flexible, high-performance ML serving. SysML Conference.
Kumar, N., et al. (2020). AI workload scheduling on Kubernetes clusters. IEEE Trans-actions on Cloud Computing.
Adapa, C.S.R. (2025). Transforming quality management with AI/ML and MDM inte-gration: A LabCorp case study. International Journal on Science and Technology (IJSAT), 16(1), 1–12.
Schwarzkopf, M., et al. (2019). Predictive autoscaling in cloud orchestration systems. USENIX ATC.
Binnig, C., et al. (2021). Optimizing model deployments for cloud-region latency. VLDB Journal.
Zhou, T., et al. (2020). AutoML orchestration in federated cloud environments. IEEE Transactions on Neural Networks.
Kim, J., et al. (2022). Telemetry-guided model training in distributed AI pipelines. IEEE Access.
Chandra Sekhara Reddy Adapa. (2025). Blockchain-Based Master Data Management: A Revolutionary Approach to Data Security and Integrity. International Journal of In-formation Technology and Management Information Systems (IJITMIS), 16(2), 1061-1076.
Wang, K., et al. (2021). Continuous monitoring for AI model drift and automated re-deployment. AAAI Workshops.
Chen, M., et al. (2019). Region-aware model management for scalable inference. Pro-ceedings of SIGMOD.
Mukesh, V. (2025). Architecting intelligent systems with integration technologies to enable seamless automation in distributed cloud environ-ments. International Journal of Advanced Research in Cloud Computing (IJARCC), 6(1),5-10.
Gulati, A., Holler, A., & Ji, M. (2012). CloudScale: Elastic resource scaling for mul-ti-tenant cloud systems. Proceedings of the 2nd ACM Symposium on Cloud Compu-ting.
Bojinov, H., et al. (2020). Deploying machine learning models in Kubernetes envi-ronments. IBM Journal of Research and Development, 64(1/2), 5–1.
Mukesh, V., Joel, D., Balaji, V. M., Tamilpriyan, R., & Yogesh Pandian, S. (2024). Data management and creation of routes for automated vehicles in smart city. Interna-tional Journal of Computer Engineering and Technology (IJCET), 15(36), 2119–2150. doi: https://doi.org/10.5281/zenodo.14993009
Adapa, C.S.R. (2025). Cloud-based master data management: Transforming enterprise data strategy. International Journal of Scientific Research in Computer Science, Engi-neering and Information Technology, 11(2), 1057–1065. https://doi.org/10.32628/CSEIT25112436
Wang, C., Yu, L., & Zhang, J. (2021). Edge-cloud synergy in AI model orchestration: A survey. IEEE Internet of Things Journal, 8(15), 11933–11949.
Mukesh, V. (2024). A Comprehensive Review of Advanced Machine Learning Tech-niques for Enhancing Cybersecurity in Blockchain Networks. ISCSITR-International Journal of Artificial Intelligence, 5(1), 1–6.
Liu, X., Ren, Y., & Jin, H. (2022). Auto-scaling strategies for AI workloads in heteroge-neous cloud platforms. Future Generation Computer Systems, 128, 123–136.
Miao, Y., Zheng, Z., & Lyu, M. R. (2020). AutoDeploy: Container-based automatic de-ployment system for AI models in hybrid clouds. IEEE Transactions on Services Computing, 13(3), 567–580.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Sankaranarayanan S, (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
