Cloud-Based AI Systems for Resilient Data Engineering: Challenges and Solutions

Abstract

In the evolving landscape of data engineering, cloud-based artificial intelligence (AI) systems have emerged as pivotal in enhancing resilience and scalability. This paper explores the challenges and solutions associated with deploying AI-driven systems in cloud environments, focusing on data reliability, system performance, and security. Cloud-based AI systems offer substantial benefits, including scalability, flexibility, and advanced analytical capabilities. However, they also encounter significant challenges such as data integrity, model drift, system robustness, and privacy concerns. We delve into these challenges, examining their implications for data engineering practices. The paper proposes a set of solutions and best practices aimed at addressing these issues, including advanced data validation techniques, continuous model retraining, and robust security frameworks. By leveraging case studies and empirical data, we provide actionable insights for practitioners and researchers to enhance the reliability and resilience of cloud-based AI systems. This study contributes to the broader understanding of AI implementation in cloud environments and offers a comprehensive overview of strategies to mitigate common challenges, ultimately advancing the field of data engineering