Data Engineering Strategies for Scaling AI-Driven OSS/BSS Platforms in Retail Manufacturing
Keywords:
AI-Driven OSS/BSS,Retail Manufacturing Data Pipelines,Scalable Data Architecture,Real-Time Data Processing,Cloud-Native Data Platforms,ETL Optimization,Data Lakehouse Integration,Microservices for OSS/BSS,Data Governance in AI Systems,Streaming Analytics,DataOps in Manufacturing,AI Model Deployment at Scale,Big Data Infrastructure,Edge Data Processing,Intelligent Workflow AutomationAbstract
In the rapidly evolving landscape of retail manufacturing, the integration of AI-driven solutions into Operational Support Systems
and Business Support Systems has become imperative to maintain competitive advantage. As these platforms scale, data
engineering strategies play a pivotal role in ensuring efficient processing, storage, and management of extensive and varied datasets.
The abstract of this work encapsulates the intricate interplay between data infrastructure and AI applications, emphasizing
methodologies that foster scalable, reliable, and responsive systems
References
Challa, S. R., Malempati, M., Sriram, H. K., & Dodda, A. (2024). Leveraging Artificial Intelligence for Secure and Efficient Payment Systems: Transforming Financial Transactions, Regulatory Compliance, and Wealth Optimization. Leveraging Artificial Intelligence for Secure and Efficient Payment Systems: Transforming Financial Transactions, Regulatory Compliance, and Wealth Optimization (December 22, 2024).
