  Machine learning (ML) has seen tremendous advancements, but its environmental
footprint remains a concern. Acknowledging the growing environmental impact of
ML this paper investigates Green ML, examining various model architectures and
hyperparameters in both training and inference phases to identify
energy-efficient practices. Our study leverages software-based power
measurements for ease of replication across diverse configurations, models and
datasets. In this paper, we examine multiple models and hardware configurations
to identify correlations across the various measurements and metrics and key
contributors to energy reduction. Our analysis offers practical guidelines for
constructing sustainable ML operations, emphasising energy consumption and
carbon footprint reductions while maintaining performance. As identified,
short-lived profiling can quantify the long-term expected energy consumption.
Moreover, model parameters can also be used to accurately estimate the expected
total energy without the need for extensive experimentation.
