Data Engineering for AI: Optimizing Data Quality and Accessibility for Machine Learning Models

Alladi Deekshith

Abstract


In the era of artificial intelligence (AI) and machine learning (ML), the significance of high-quality and accessible data cannot be overstated. This paper explores the essential practices and methodologies for optimizing data quality and accessibility within data engineering frameworks tailored for AI applications. We examine the critical dimensions of data quality, including accuracy, completeness, consistency, and timeliness, and how these factors influence the performance of machine learning models. Furthermore, we discuss strategies for improving data accessibility, such as data integration, storage solutions, and effective data governance. By implementing these best practices, organizations can enhance the reliability of their data pipelines, thereby facilitating the development of robust AI systems that deliver actionable insights and drive decision-making processes.


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