AI-Driven Data Sampling Strategies for Efficient Model Training

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) are widely used across industries such as healthcare, finance, manufacturing, transportation, cybersecurity, and scientific computing to solve complex problems. The effectiveness of ML models depends heavily on the quality and representativeness of training data. However, the rapid growth of big data has increased computational costs, memory requirements, and training times, making traditional full-dataset training inefficient.To address these challenges, intelligent data sampling has emerged as an important research area. AI-based sampling techniques—including random sampling, stratified sampling, importance sampling, active learning, uncertainty sampling, reinforcement learning-based sampling, and adaptive data selection—identify the most informative data points while reducing redundancy. These methods improve computational efficiency, enhance class representation, and accelerate model training without significantly affecting accuracy. This study reviews the evolution of AI-driven sampling strategies and proposes an adaptive framework that combines statistical sampling with intelligent decision-making mechanisms for real-time data selection. Experimental results demonstrate that intelligent sampling can reduce training data requirements by 30–60% while maintaining over 95% of the predictive performance achieved using full datasets. The approach also improves scalability, reduces energy consumption, and supports efficient deployment of modern machine learning systems. Overall, AI-based sampling provides a practical and effective solution for optimizing data usage and enhancing machine learning performance in large-scale applications.