Abstract

The utilization of Artificial Intelligence (AI) in current systems carries significant dependence upon the stability of data engineering technique. This article discusses the importance of improving the efficiency of data pipelines to improve AI applications with reference to some sample predictive analytics analytics. Therefore, through reviewing various real-world applications in the article, critical issues like data latency, inconsistency, and scalability, which affect the value of AI models, have been noted. These yield problems are discussed and real-time data processing, autopipe, and other data engineering methods that deal with such problems are explained in its details. These shed light on how the practices enhance accuracy of AI model, operations efficiency and real time decisions. This study therefore makes a call for fine tuning of data pipelines in the effort to achieve optimal usage of AI in various fields.