International Journal on Science and Technology

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Container-Orchestrated Microservices for Large-Scale AI-Driven Retail Applications

Author(s) Udit Agarwal, Aditya Gupta
Country United States
Abstract The contemporary retail environment is characterized by intense competitive pressure and escalating customer expectations, demanding extreme IT and business agility. This report details the architectural convergence necessary to meet these demands: the integration of Artificial Intelligence (AI), Microservices Architecture (MSA), and Container Orchestration. AI advancements, coupled with reduced processing costs, present a significant opportunity to drive profitability. However, operationalizing large-scale AI applications, such as dynamic pricing and real-time recommendation engines, necessitates a robust, scalable, and resilient deployment platform. This white paper outlines how MSA, characterized by polyglot persistence and domain-driven design, provides the requisite modularity and independent evolution. Container orchestration tools, specifically Kubernetes, are commonly used for managing the ensuing complexity, automating deployment, scaling, self-healing, and ensuring consistency across environments. Furthermore, the successful realization of low-latency retail systems depends upon mature MLOps practices, advanced observability via distributed tracing, and sophisticated resilience patterns, including API Gateways, Service Meshes, and the Saga pattern for distributed data integrity.
Keywords Microservices Architecture, Container Orchestration, Kubernetes, AI Deployment, MLOps, Retail Applications, Dynamic Pricing, Real-Time Recommendations, Scalability, Resilience, Latency.
Field Engineering
Published In Volume 14, Issue 1, January-March 2023
Published On 2023-01-05
DOI https://doi.org/10.71097/IJSAT.v14.i1.10087
Short DOI https://doi.org/hbkrg3
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