Carbon-Aware Machine Learning Model Optimization for Sustainable AI Systems

The rapid expansion of machine learning (ML) applications has led to a significant increase in computational resource consumption, resulting in substantial carbon emissions. This paper introduces a novel carbon-aware optimization framework that integrates environmental impact as a primary constraint during model training and deployment. Unlike traditional optimization approaches that focus solely on accuracy and latency, the proposed method incorporates carbon intensity signals, energy-efficient scheduling, and adaptive model compression techniques to minimize emissions without compromising performance. The framework dynamically adjusts training workloads based on real-time energy grid carbon intensity and employs multi-objective optimization to balance accuracy, energy consumption, and environmental impact. Experimental evaluations demonstrate that the proposed approach reduces carbon emissions by up to 35% while maintaining competitive model accuracy. This work contributes toward sustainable AI by embedding carbon-awareness into the ML lifecycle.