Measuring and Modeling CO2 Emissions in Machine Learning Processes

With the rapid expansion of the computing industry, efficient energy utilization and reduction of CO2 emissions are critically important. This research develops analytical tools to predict CO2 emissions from various machine learning processes. We present a novel methodology for data acquisition and analysis of CO2 emissions during model training and testing. Our results demonstrate the environmental impact of different algorithms and provide insights into optimizing energy consumption in artificial intelligence applications.