Optimizing cloud-based machine learning pipelines with generative AI: Innovations in automated data augmentation and model enhancement

Abstract

Integrating Generative AI into Cloud-based ML pipelines is a revolutionary way of improving data augmentation and model improvement. Realistic synthetic data has been more accessible to generate through Generative models such as GANs and VAE because of their ability to generate synthetic data with higher quality and variability than traditional generative models. In this paper, I will discuss the adoptions that have been made utilizing generative AI in enhancing the data augmentation process and making robust models together with handling factors such as data bias and computational demands. It also explains future trends and directions, such as real-time generative AI, edge computing, and AI ethical practices. By tackling these difficulties and using the possibilities of generative AI, it is possible to improve the efficacy, the possibility of scale, and flexibility of technological systems of machine learning and create a more effective alliance between artificial intelligence and industries.