A Framework for Intelligent Data Processing: Leveraging Data Engineering and Machine Learning Techniques for Advanced Artificial Intelligence Applications
Keywords:
Data Engineering, Machine Learning, Artificial Intelligence, Data Pipelines, Intelligent Data ProcessingAbstract
The rapid growth of digital data has created significant opportunities for developing intelligent systems capable of extracting insights and supporting automated decision-making. However, raw data alone cannot produce meaningful outcomes without structured data processing pipelines that combine robust data engineering with advanced machine learning techniques. This paper proposes a conceptual framework for intelligent data processing that integrates data engineering processes such as data ingestion, cleaning, transformation, and storage with machine learning workflows including feature engineering, model training, and predictive analytics.
The proposed framework emphasizes scalability, automation, and data quality to support advanced artificial intelligence applications across domains such as healthcare, finance, and smart cities. Through a layered architecture model, the framework illustrates how modern data infrastructures can enable efficient data flow from collection to intelligent decision-making. The study synthesizes existing research on data engineering pipelines and machine learning architectures while presenting a unified system model for AI-driven applications. The findings highlight that combining robust data engineering practices with machine learning methodologies significantly enhances data reliability, model performance, and system scalability.
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