# Improving Automated Machine-Learning Systems through Green AI
###### Abstract
To mitigate these environmental impacts, it becomes imperative to devise techniques that exhibit higher resource efficiency and enable more rapid evaluations, consequently discarding approaches with subpar performance. The major proportion of energy consumption and carbon emissions originates from the AutoML process, involving pipeline evaluations and the storage of intermediate search results. However, when formulating an AutoML proposal, it is crucial to adopt a holistic perspective considering the entire lifecycle, which includes data generation, storage, computational efforts, memory requirements during development, and the final benchmarking. It is essential not to judge research initiatives with a significant environmental footprint without carefully evaluating their cost-benefit trade-offs. This paper primarily centers on the AutoML process, aligning with the principle of Pareto, also known as the 80-20 rule and the law of the vital few [16]. The analysis of resource consumption in the context of AutoML is an essential aspect for evaluating the environmental sustainability of automated machine-learning approaches and contributes to the quest for more eco-friendly solutions in the field of artificial intelligence. Various strategies have been developed to assess environmental sustainability in the context of AutoML, aiming to identify areas of higher environmental impact and achieve greater optimization for sustainability. Among the most relevant ones are the measurement of computational resource consumption, estimation of carbon emissions, evaluation of energy efficiency, scalability, and analysis of the AutoML lifecycle (Figure 1). Additionally, the performance and quality of models generated by AutoML are compared with the computational effort required. It is also suggested to conduct a comparison of environmental sustainability with traditional manual approaches to understand the environmental advantages of automation in artificial intelligence. AutoML should not be quantified solely based on the efficiency of an approach or the environmental impact of a specific experiment. Instead, both factors should be considered jointly as research continues to explore the intersection of efficiency and environmental impact [17,18]. A burgeoning solution to the challenge of energy-intensive algorithms is green or sustainable AI [19, 20, 21, 22], which underscores the growing pursuit of ecologically mindful practices within the realm of AI and AutoML. Key strategies include precision-energy trade-offs, energy-aware neural architecture search, model compression, and multi-objective Pareto optimization considering accuracy, latency, and power [23]. Cross-domain transfer learning can also improve sample efficiency and lower training costs [24]. Overall, research toward responsible and eco-conscious AI is rapidly gaining traction. The integration of Green AI principles into hyperparameter optimization and AutoML offers significant potential to mitigate the environmental footprint of developing performant ML solutions. The objective of this research is to explore the sustainability implications associated with the computational processes within the domain of Automated Machine Learning. In this paper, we have made the following contributions:  
* The best hyperparameter optimization strategy was identified using Green AI criteria among frequently used algorithms: Bayesian search and random search in scikit-learn. * A proof of concept is presented under a Green AI paradigm for the entire AutoML process. * An experimentation process was implemented based on the principles of Green AI sustainability, which confirmed the validity of sustainability applied to experimentation in AutoML. Figure 1: Illustrates the AutoML lifecycle, encompassing data generation and storage, algorithm and configuration selection, model training and evaluation, and model implementation. Notably, it features a feedback loop that cycles back to the selection of algorithms and configurations after the model training and evaluation stage.