DATAFIRST ML: A NEW PARADIGM FOR INTELLIGENT SMART APPLICATIONS

Abstract

Next-generation smart applications require intelligent systems capable of handling massive, heterogeneous, and continuously evolving data. Traditional machine learning approaches often focus on algorithmic improvements while underutilizing the importance of data quality and management. This paper proposes a data-centric machine learning framework designed for smart applications that demand high accuracy, scalability, and reliability. The framework emphasizes data preprocessing, feature engineering, and continuous data refinement to improve learning performance. Machine learning models are trained using structured and unstructured data sources. Experimental evaluation is conducted on smart application datasets to validate performance. The proposed framework is compared with conventional machine learning models. Results demonstrate improved accuracy, reduced processing time, and efficient resource utilization. The study highlights the significance of data-centric design for intelligent systems. The framework supports scalable and real-time smart application deployment.