Impact Analysis of Generative AI on the Accuracy and Scalability of Machine Learning Models

Generative AI has become a fundamental tool in machine learning which address the issues of data scarcity, model generalization, providing additional model efficacy and allowing them to scale horizontally in nature. In this paper, presents the effect of generative AI on the performance of machine learning models with respect to improved accuracy and enhanced scalability. Advanced generative AI methods including GANs and VAEs are examined for data augmentation, model training, and other robustness improvements. The research shows that integrating generative AI with machine learning pipelines can dramatically boost the performance of machine learning models, especially in data-scarce settings. This enhances the scalability of the models, allowing them to perform better on larger and more complex datasets. These results indicate the potential significance of generative AI in paving the way for better application of machine learning, enabling a streamlined model while being generalizable to a wider scope of real-world systems.