Leveraging Generative AI for Scalable Data Quality Enhancement and Intelligent Augmentation in Enterprise AI Systems

The combination of generative AI and data engineering has the potential to create a new breed of data that can be effectively applied at scale through effective use of high-quality augmentation as well as AI-driven pipelines. This post examines cutting-edge artificial intelligence (AI) capabilities, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs) and diffusion models integrated within AWS cloud tools, data warehousing with Snowflake, and Apache Airflow to act as the orchestration layer. It also integrates GitHub CI/CD through Push to deploy the model and utilize MLOps workflow. Solution Overview: The Proposed Framework Enriches Real-Time Pipelines that Automate Data Quality and Fairness with Outputs for Both Enterprise AI and Cloud Pipeline Paths for Users in Healthcare, Financial Services, Retail, Manufacturing, Construction Materials, and Gaming.