Advancements in Data Modeling for Machine Learning and AI: Techniques, Challenges, and Future Directions

Vijay Arpudaraj Antonyraj

Abstract


The rapid evolution of Machine Learning (ML) and Artificial Intelligence (AI) has driven significant progress in various industries, from healthcare to finance. Central to the success of these technologies is effective data modeling, which serves as the foundation for training and optimizing algorithms. This paper explores the latest advancements in data modeling techniques, focusing on how they are applied to ML and AI systems. It examines key methodologies such as supervised and unsupervised learning, deep learning architectures, and reinforcement learning, while also addressing challenges such as data sparsity, bias, and scalability. Furthermore, the paper highlights the integration of novel data modeling approaches like transfer learning and explainable AI (XAI) to improve model transparency and performance. Finally, the research identifies emerging trends, including the use of synthetic data, edge computing, and federated learning, offering a comprehensive roadmap for future advancements in the field. Through this exploration, the paper aims to provide a holistic understanding of the role of data modeling in shaping the future of AI and ML applications.

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