Engineering Carbon Emission-aware Machine Learning Pipelines

Abstract

The proliferation of machine learning (ML) has brought unprecedented advancements in technology, but it has also raised concerns about its environmental impact, particularly concerning carbon emissions. To address the imperative of environmentally responsible ML, we present in this paper a novel ML pipeline, named CEMAI, designed to monitor and analyze carbon emissions across the entire lifecycle of ML model development, from data preparation to training and deployment. Our endeavor involves an exhaustive evaluation process underpinned by three industrial case studies. These case studies are structured around the application of ML models to predict tool wear, estimate remaining useful lifetimes, and detect anomalies in the Industrial Internet of Things (IIoT). Leveraging sensor data originating from CNC machining and broaching operations, our research shows empirically the efficacy of carbon emissions as a dependable metric guiding the configuration of an ML development process. The essence of our approach lies in striking a balance between superior performance and minimal carbon emissions. Our findings reveal the potential to optimize pipeline configurations for ML models in a manner that not only enhances performance but also drastically reduces carbon emissions, thereby underlining the significance of adopting ecologically responsible engineering practices.