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

Author(s) Urvangkumar Kothari
Country United States
Abstract 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.
Keywords Generative AI, Data Engineering, GANs, VAEs, Diffusion Models, AWS Sage Maker, Snowflake, Apache Airflow, CI/CD, MLOps, AI Pipeline, Data Augmentation, Bias Mitigation, Industry Applications
Field Engineering
Published In Volume 5, Issue 12, December 2024
Published On 2024-12-04
Cite This Leveraging Generative AI for Scalable Data Quality Enhancement and Intelligent Augmentation in Enterprise AI Systems - Urvangkumar Kothari - IJLRP Volume 5, Issue 12, December 2024. DOI 10.70528/IJLRP.v5.i12.1416
DOI https://doi.org/10.70528/IJLRP.v5.i12.1416
Short DOI https://doi.org/g88sdw
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