Bridging Model-Centric and Data-Centric AI: A Unified Framework for Scalable Real-World Deployment
Abstract: This paper presents a unified framework that bridges model-centric and data-centric approaches in artificial intelligence (AI), addressing the increasing need for scalable and deployment-ready AI systems. While the model-centric paradigm emphasizes novel architectures and algorithms, data-centric AI focuses on improving data quality for better performance. The proposed framework combines both, offering modularity, interpretability, and performance robustness across diverse environments. The framework is validated through empirical experiments involving large-scale datasets and modern deep learning models. Findings suggest a significant uplift in generalization, reproducibility, and deployment efficiency across domains such as computer vision and NLP.
Keywords: Model-centric AI, data-centric AI, deployment, scalability, unified framework, MLOps
How to Cite?: Sachin Samrat Medavarapu, "Bridging Model-Centric and Data-Centric AI: A Unified Framework for Scalable Real-World Deployment", Volume 14 Issue 5, May 2025, International Journal of Science and Research (IJSR), Pages: 1794-1797, https://www.ijsr.net/getabstract.php?paperid=SR25528130124, DOI: https://dx.doi.org/10.21275/SR25528130124