AI and Data Engineering: Harnessing the Power of Machine Learning in Data-Driven Enterprises

Abstract

As the volume, variety, and velocity of data continue to grow in the digital age, enterprises increasingly rely on advanced data engineering and machine learning techniques to extract actionable insights. This paper explores the intersection of Artificial Intelligence (AI) and data engineering, focusing on how machine learning algorithms are transforming data-driven enterprises. By integrating AI models into data pipelines, organizations can automate decisionmaking processes, improve data processing efficiency, and enhance the accuracy of predictive analytics. Key AI models, including Random Forests, Gradient Boosting Machines, and Deep Learning architectures such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, are assessed for their effectiveness in solving complex data challenges. The study highlights the benefits of AI-driven data pipelines and provides practical insights into optimizing machine learning workflows for enterprise-scale applications.