Optimizing Data Pipelines for Generative AI Workflows: Challenges and Best Practices

Author(s)	Venkata Nagendra Kumar Kundavaram
Country	United States
Abstract	The research studies optimizing data pipelines for generative AI workflows within the U.S. retail industry, focusing on challenges, impacts, and best practices. The study describes robust pipelines that can serve to further boost efficiency and scalability and ensure full compliance with such critical applications as inventory optimization, dynamic pricing tools, and personalized retail services. It underlines scalability challenges, latency, and regulatory requirements as strong determinants of modern cloud-based infrastructures. Furthermore, best practices such as distributed computing, real-time monitoring, and secure data handling are considered important in relation to the pipeline's reliability. Thematic analysis of secondary data provides actionable insights to advance innovation in retail by deploying generative AI for operational performance.
Keywords	Generative AI, Data Pipelines, Inventory management, Optimization, RetailIndustry, Cloud-based architecture
Field	Engineering
Published In	Volume 16, Issue 1, January-March 2025
Published On	2025-01-22
Cite This	Optimizing Data Pipelines for Generative AI Workflows: Challenges and Best Practices - Venkata Nagendra Kumar Kundavaram - IJSAT Volume 16, Issue 1, January-March 2025. DOI 10.71097/IJSAT.v16.i1.1527
DOI	https://doi.org/10.71097/IJSAT.v16.i1.1527
Short DOI	https://doi.org/g83jhm

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