Energy-Efficient AI Development using Python: Methods and Case Studies

With the rapid development and adoption of artificial intelligence (AI), the environmental impact of energy consumption during the training and deployment of AI models has become a pressing concern. Energy-efficient AI development is crucial for reducing the carbon footprint of AI technologies. This paper explores various methods and tools used to optimize energy efficiency in AI model development, focusing on Python-based techniques. Case studies are presented to demonstrate practical applications of energy-efficient AI. By analyzing optimization strategies such as model pruning, quantization, hardware acceleration, and energy-aware training, this work highlights how Python libraries and frameworks can contribute to sustainable AI practices.