Building Scalable Data Infrastructure for Generative AI Models: Challenges and Solutions

Abstract

The rapid advancement of Generative AI models has underscored the necessity for robust and scalable data infrastructures capable of managing vast datasets and complex computational requirements. This paper explores the unique challenges encountered in building such infrastructures, including data acquisition, storage, processing, and real-time access. We analyze existing solutions and propose best practices for designing architectures that ensure efficiency, scalability, and reliability. By examining case studies and current industry practices, the paper provides a comprehensive framework for developing data infrastructures tailored to the demands of Generative AI applications.