Ensuring Data Reliability in AI-Powered Cloud Architectures: Development of An Innovative Framework
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In the rapidly evolving landscape of cloud computing, the integration of Artificial Intelligence (AI) has become essential for enhancing data-driven decision-making and improving operational efficiency. However, ensuring data reliability in AI-powered cloud architectures remains a significant challenge, as the performance of AI models heavily relies on the integrity, accuracy, and availability of the underlying data. This research aims to develop an innovative framework designed to enhance data reliability within AI-driven cloud environments. The proposed framework incorporates advanced techniques such as real-time data validation, error detection, and fault tolerance mechanisms to address common issues like data inconsistency, loss, and corruption. By leveraging both AI models and cloud infrastructure best practices, the framework seeks to provide a robust solution for maintaining data integrity and ensuring uninterrupted AI performance. The results of this study demonstrate the framework’s effectiveness in improving data reliability, reducing error rates, and enhancing the overall efficiency of AI systems in cloud environments. This work offers valuable insights for organizations seeking to adopt AI technologies while maintaining high standards of data reliability, with implications for both cloud service providers and AI developers. Future research directions focus on refining the framework for scalability and exploring its application in diverse industries.
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Copyright (c) 2019 Dillep kumar Pentyala
This work is licensed under a Creative Commons Attribution 4.0 International License.
