Edge AI Deployment Pipelines for Real-Time In-Store Retail Analytics

Author(s)	Udit Agarwal
Country	United States
Abstract	This paper explores the role of Edge Artificial Intelligence (Edge AI) in enabling real-time in- store retail analytics. By processing data at the network edge, retailers can achieve reduced latency, enhanced data privacy, and improved customer experiences. The paper discusses deployment pipelines for Edge AI, including data acquisition, model optimization, inference execution, and integration with retail management systems. A detailed analysis of implementation challenges and opportunities for future scalability is presented. The proposed framework demonstrates how Edge AI can optimize inventory tracking, shopper behavior analysis, and operational decision-making in physical retail environments.
Keywords	Edge AI, Retail Analytics, Real-Time Processing, Deployment Pipelines, Computer Vision.
Field	Engineering
Published In	Volume 4, Issue 6, November-December 2022
Published On	2022-12-03
DOI	https://doi.org/10.36948/ijfmr.2022.v04i06.67507

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