Harnessing Advanced Cloud Computing for the Scalable Deployment of Generative AI: Enhancing Computational Efficiency and Real-Time Applications

References

[1] Agrawal, A., Gans, J., & Goldfarb, A. (2020). The Economics of Artificial Intelligence: An Agenda. University of Chicago Press. https://doi.org/10.7208/9780226613475

[2] Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and Machine Learning: Limitations and Opportunities. MIT Press.

[3] Buyya, R., & Di Martino, B. (2019). Cloud Computing: Principles and Paradigms. Wiley. https://doi.org/10.1002/9780470940105

[4] Dean, J., Corrado, G., & Monga, R. (2019). Large Scale Distributed Deep Networks. Advances in Neural Information Processing Systems (NIPS).

[5] Gartner, I. T. (2022). Top Strategic Technology Trends for 2022. Gartner Reports. https://www.gartner.com/en/newsroom/press-releases/2022

[6] Goodfellow, I., Bengio, Y., & Courville, A. (2019). Deep Learning. MIT Press.

[7] Google Cloud. (2023). Best Practices for Running AI Models on Multi-Cloud Architectures. Google Cloud Documentation. https://cloud.google.com/ai/multicloud-strategies

[8] Green, B., & Gill, H. (2022). Serverless Architectures for Cost-Effective AI Deployments. ACM Cloud Computing Journal, 47(3), 198-212.

[9] He, K., Zhang, X., Ren, S., & Sun, J. (2020). Deep Residual Learning for Image Recognition. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR.2016.90

[10] Joshi, A., & Srivastava, R. (2021). Scalable Cloud Infrastructure for AI. IEEE Cloud Computing, 8(3), 24-32. https://doi.org/10.1109/MCC.2021.3072913

[11] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems. https://doi.org/10.1145/3065386

[12] LeCun, Y., Bengio, Y., & Hinton, G. (2021). Deep Learning. Nature, 521, 436–444. https://doi.org/10.1038/nature14539

[13] Microsoft Azure. (2023). AI at the Edge: Deploying Real-Time AI with Edge Computing. Microsoft Azure Blog. https://azure.microsoft.com/en-us/blog/ai-at-the-edge/

[14] Mohanty, S., & Bhatt, R. (2022). Edge AI: Real-Time Machine Learning at the Edge. O'Reilly Media.

[15] Shen, C., & Tomasevic, M. (2021). Optimizing AI Workloads with Serverless Computing. IEEE Software Engineering Journal, 56(4), 57-64. https://doi.org/10.1109/ICSE.2021.0923

[16] Krishna, K. (2020). Towards Autonomous AI: Unifying Reinforcement Learning, Generative Models, and Explainable AI for Next-Generation Systems. Journal of Emerging Technologies and Innovative Research, 7(4), 60-61.

[17] Murthy, P. (2020). Optimizing cloud resource allocation using advanced AI techniques: A comparative study of reinforcement learning and genetic algorithms in multi-cloud environments. World Journal of Advanced Research and Reviews. https://doi. org/10.30574/wjarr, 2.

[18] MURTHY, P., & BOBBA, S. (2021). AI-Powered Predictive Scaling in Cloud Computing: Enhancing Efficiency through Real-Time Workload Forecasting.

[19] Mehra, A. D. (2020). UNIFYING ADVERSARIAL ROBUSTNESS AND INTERPRETABILITY IN DEEP NEURAL NETWORKS: A COMPREHENSIVE FRAMEWORK FOR EXPLAINABLE AND SECURE MACHINE LEARNING MODELS. International Research Journal of Modernization in Engineering Technology and Science, 2.

[20] Mehra, A. (2021). Uncertainty quantification in deep neural networks: Techniques and applications in autonomous decision-making systems. World Journal of Advanced Research and Reviews, 11(3), 482-490.

[21] Thakur, D. (2020). Optimizing Query Performance in Distributed Databases Using Machine Learning Techniques: A Comprehensive Analysis and Implementation. Iconic Research And Engineering Journals, 3, 12.

[22] Krishna, K. (2022). Optimizing query performance in distributed NoSQL databases through adaptive indexing and data partitioning techniques. International Journal of Creative Research Thoughts (IJCRT). https://ijcrt. org/viewfulltext. php.

[23] Krishna, K., & Thakur, D. (2021). Automated Machine Learning (AutoML) for Real-Time Data Streams: Challenges and Innovations in Online Learning Algorithms. Journal of Emerging Technologies and Innovative Research (JETIR), 8(12).

[24] Murthy, P., & Mehra, A. (2021). Exploring Neuromorphic Computing for Ultra-Low Latency Transaction Processing in Edge Database Architectures. Journal of Emerging Technologies and Innovative Research, 8(1), 25-26.

[25] Thakur, D. (2021). Federated Learning and Privacy-Preserving AI: Challenges and Solutions in Distributed Machine Learning. International Journal of All Research Education and Scientific Methods (IJARESM), 9(6), 3763-3764.

[26] KRISHNA, K., MEHRA, A., SARKER, M., & MISHRA, L. (2023). Cloud-Based Reinforcement Learning for Autonomous Systems: Implementing Generative AI for Real-time Decision Making and Adaptation.

[27] THAKUR, D., MEHRA, A., CHOUDHARY, R., & SARKER, M. (2023). Generative AI in Software Engineering: Revolutionizing Test Case Generation and Validation Techniques.

[28] Krishna, K., & Murthy, P. (2022). AIENHANCED EDGE COMPUTING: BRIDGING THE GAP BETWEEN CLOUD AND EDGE WITH DISTRIBUTED INTELLIGENCE. TIJER-INTERNATIONAL RESEARCH JOURNAL, 9 (2).

[29] Murthy, P., & Thakur, D. (2022). Cross-Layer Optimization Techniques for Enhancing Consistency and Performance in Distributed NoSQL Database. International Journal of Enhanced Research in Management & Computer Applications, 35.

[30] MURTHY, P., MEHRA, A., & MISHRA, L. (2023). Resource Allocation for Generative AI Workloads: Advanced Cloud Resource Management Strategies for Optimized Model Performance.

[31] Alahari, J., Thakur, D., Goel, P., Chintha, V. R., & Kolli, R. K. (2022). Enhancing iOS Application Performance through Swift UI: Transitioning from Objective-C to Swift. In International Journal for Research Publication & Seminar, 13 (5): 312. https://doi. org/10.36676/jrps. v13. i5. 15 (Vol. 4).

[32] Salunkhe, V., Thakur, D., Krishna, K., Goel, O., & Jain, A. (2023). Optimizing Cloud-Based Clinical Platforms: Best Practices for HIPAA and HITRUST Compliance. Innovative Research Thoughts, 9 (5): 247. https://doi. org/10.36676/irt. v9. i5, 1486.

[33] Agrawal, S., Thakur, D., Krishna, K., & Singh, S. P. Enhancing Supply Chain Resilience through Digital Transformation.

[34] Krishna, K. (2020). Towards Autonomous AI: Unifying Reinforcement Learning, Generative Models, and Explainable AI for Next-Generation Systems. Journal of Emerging Technologies and Innovative Research, 7(4), 60-61.

[35] Murthy, P. (2020). Optimizing cloud resource allocation using advanced AI techniques: A comparative study of reinforcement learning and genetic algorithms in multi-cloud environments. World Journal of Advanced Research and Reviews. https://doi. org/10.30574/wjarr, 2.

[36] MURTHY, P., & BOBBA, S. (2021). AI-Powered Predictive Scaling in Cloud Computing: Enhancing Efficiency through Real-Time Workload Forecasting.

[37] Mehra, A. D. (2020). UNIFYING ADVERSARIAL ROBUSTNESS AND INTERPRETABILITY IN DEEP NEURAL NETWORKS: A COMPREHENSIVE FRAMEWORK FOR EXPLAINABLE AND SECURE MACHINE LEARNING MODELS. International Research Journal of Modernization in Engineering Technology and Science, 2.

[38] Mehra, A. (2021). Uncertainty quantification in deep neural networks: Techniques and applications in autonomous decision-making systems. World Journal of Advanced Research and Reviews, 11(3), 482-490.

[39] Thakur, D. (2020). Optimizing Query Performance in Distributed Databases Using Machine Learning Techniques: A Comprehensive Analysis and Implementation. Iconic Research And Engineering Journals, 3, 12.

[40] Krishna, K. (2022). Optimizing query performance in distributed NoSQL databases through adaptive indexing and data partitioning techniques. International Journal of Creative Research Thoughts (IJCRT). https://ijcrt. org/viewfulltext. php.

[41] Krishna, K., & Thakur, D. (2021). Automated Machine Learning (AutoML) for Real-Time Data Streams: Challenges and Innovations in Online Learning Algorithms. Journal of Emerging Technologies and Innovative Research (JETIR), 8(12).

[42] Murthy, P., & Mehra, A. (2021). Exploring Neuromorphic Computing for Ultra-Low Latency Transaction Processing in Edge Database Architectures. Journal of Emerging Technologies and Innovative Research, 8(1), 25-26.

[43] Thakur, D. (2021). Federated Learning and Privacy-Preserving AI: Challenges and Solutions in Distributed Machine Learning. International Journal of All Research Education and Scientific Methods (IJARESM), 9(6), 3763-3764.

[44] KRISHNA, K., MEHRA, A., SARKER, M., & MISHRA, L. (2023). Cloud-Based Reinforcement Learning for Autonomous Systems: Implementing Generative AI for Real-time Decision Making and Adaptation.

[45] THAKUR, D., MEHRA, A., CHOUDHARY, R., & SARKER, M. (2023). Generative AI in Software Engineering: Revolutionizing Test Case Generation and Validation Techniques.

[46] Krishna, K., & Murthy, P. (2022). AIENHANCED EDGE COMPUTING: BRIDGING THE GAP BETWEEN CLOUD AND EDGE WITH DISTRIBUTED INTELLIGENCE. TIJER-INTERNATIONAL RESEARCH JOURNAL, 9 (2).

[47] Murthy, P., & Thakur, D. (2022). Cross-Layer Optimization Techniques for Enhancing Consistency and Performance in Distributed NoSQL Database. International Journal of Enhanced Research in Management & Computer Applications, 35.

[48] MURTHY, P., MEHRA, A., & MISHRA, L. (2023). Resource Allocation for Generative AI Workloads: Advanced Cloud Resource Management Strategies for Optimized Model Performance.

[49] Alahari, J., Thakur, D., Goel, P., Chintha, V. R., & Kolli, R. K. (2022). Enhancing iOS Application Performance through Swift UI: Transitioning from Objective-C to Swift. In International Journal for Research Publication & Seminar, 13 (5): 312. https://doi. org/10.36676/jrps. v13. i5. 15 (Vol. 4).

[50] Salunkhe, V., Thakur, D., Krishna, K., Goel, O., & Jain, A. (2023). Optimizing Cloud-Based Clinical Platforms: Best Practices for HIPAA and HITRUST Compliance. Innovative Research Thoughts, 9 (5): 247. https://doi. org/10.36676/irt. v9. i5, 1486.

[51] Agrawal, S., Thakur, D., Krishna, K., & Singh, S. P. Enhancing Supply Chain Resilience through Digital Transformation.

@article{1704844,
      author = {Thejaswi Adimulam},
      title = {Harnessing Advanced Cloud Computing for the Scalable Deployment of Generative AI: Enhancing Computational Efficiency and Real-Time Applications},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {7},
      number = {1},
      pages = {628-638},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1704844.pdf},
      abstract = {Natural language processing, computer vision, and healthcare, finance, and entertainment industries rank Generative Artificial Intelligence (AI) as a cornerstone of their businesses. Unfortunately, with the complexity of the AI models growing and their scale, computing resources are needed to train them and keep them running high. By promoting scalability, affordability, and efficiency of deployable models, advanced architectures such as serverless, multi-cloud, and edge computing represent a rung on the ladder to overcoming the above challenges cloud computing provides. In this paper, we show how the deployment of generative AI can be expedited by leveraging advanced cloud computing architectures for computational efficiency, real-time applications, and scalability. In this work, we look at some cloud computing paradigms and discuss their pros and cons for deploying AI systems. Real-world use cases and experiment results of generative AI show how cloud technology can overcome the haywire needs of generative AI and serve to accelerate the innovation cycle and operate more efficiently. We also present a framework for executing scalable AI solutions on cloud infrastructures, tackling key issues such as latency, data privacy, and cost optimization.},
      keywords = {Generative AI, Cloud computing, Scalability, Real-time application, Computational efficiency},
      month = {July},
  }