SYNTHETIC DATA GENERATION FOR QUALITY ASSURANCE IN LARGESCALE AI MODELS

The Synthetic data creation has become a significant solution to quality control of large-scale AI models,
especially where data from real-world situations is not available, sensitive, or unobtainable. The application
of synthetic data in robustifying models to be less biased and more generalizable across various applications
such as healthcare, finance, and autonomous systems is what this article focus on. It emphasizes different
machine learning methods, including Generative Adversarial Networks (GANs) and Variational
Autoencoders (VAEs), that drive synthetic data generation. The paper also discusses the most important
challenges like data fidelity, privacy, and evaluation methods. According to the review of recent
developments and practical application, the paper emphasizes the capability of synthetic data to efficiently
optimize AI model training, validation, and deployment while being ethical and regulatory compliant. The
research adds to the general discussion of AI model reliability, with focus on synthetic data as a revolutionary
way of addressing data availability and quality-related risk.