AI-Powered Data Science Frameworks for Cloud-Optimized Data Management

Abstract

In the evolving landscape of cloud computing, the integration of artificial intelligence (AI) into data science frameworks is transforming the way data is managed, processed, and utilized. This paper presents an advanced AI-powered data science framework specifically designed to optimize data management within cloud environments. By leveraging machine learning algorithms, automated data preprocessing, and intelligent resource allocation, our framework enhances the efficiency, scalability, and performance of data management tasks. We explore how AI-driven techniques can address key challenges such as data heterogeneity, real-time processing demands, and resource constraints. The proposed framework incorporates adaptive learning mechanisms that continuously refine data handling processes based on evolving workloads and data characteristics. Through comprehensive case studies and performance evaluations, we demonstrate the framework's capability to achieve significant improvements in data throughput, system responsiveness, and operational cost-effectiveness. Our findings suggest that the integration of AI into cloud data science frameworks not only streamlines data management but also unlocks new potentials for innovation and optimization in cloud-based applications.