Overview
The use of machine learning models both in academia and industry is on the rise. And so is the environmental impact of ML models. While deploying a model to production, it is important to be able to estimate its carbon footprint and to understand the costs involved in running it. Balancing performance and carbon efficiency of ML models becomes critical to ensure that the benefits of ML are maximized while minimizing its environmental costs. This project proposes to develop a procedure that automatically discovers and quantifies this trade-off.
