• Jeyasri Sekar

This research aims to explore and develop effective multi-cloud strategies for optimizing distributed AI workflows and applications. The primary goal is to enhance the performance, scalability, and reliability of AI workflows by leveraging the strengths of multiple cloud service providers. The approach involves designing a comprehensive framework that integrates various multi-cloud strategies tailored for distributed AI workflows. This includes the selection and combination of cloud services, data distribution techniques, and workflow orchestration methods. The framework is implemented and tested using real-world AI applications across different cloud environments. Performance metrics such as latency, throughput, and cost-efficiency are used to evaluate the effectiveness of the proposed strategies. The study reveals that multi-cloud strategies significantly improve the overall performance and resilience of distributed AI workflows. Key findings indicate a reduction in latency by up to 30%, an increase in throughput by 25%, and cost savings of approximately 20% compared to single-cloud deployments. Additionally, the flexibility to dynamically switch between cloud providers based on workload demands enhances reliability and fault tolerance. The research concludes that adopting multi-cloud strategies for distributed AI workflows provides substantial benefits in terms of performance, scalability, and cost-efficiency. These findings have important implications for organizations looking to optimize their AI operations and leverage the diverse capabilities of different cloud service providers. Future work will focus on refining the framework and exploring advanced orchestration techniques to further enhance multi-cloud AI workflows.

Multi-cloud strategies, distributed AI workflows, cloud service providers, performance optimization, scalability

"MULTI-CLOUD STRATEGIES FOR DISTRIBUTED AI WORKFLOWS AND APPLICATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.p600-p610, May 2023, Available :http://www.jetir.org/papers/JETIR2305G82.pdf

"MULTI-CLOUD STRATEGIES FOR DISTRIBUTED AI WORKFLOWS AND APPLICATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppp600-p610, May 2023, Available at : http://www.jetir.org/papers/JETIR2305G82.pdf