How Green is AutoML for Tabular Data?
Felix Neutatz
Marius Lindauer
Ziawasch Abedjan
AutoML has risen to one of the most commonly used tools for day-to-day data science pipeline development and several popular packages exist. While AutoML systems support data scientists during the tedious process of pipeline generation, it can lead to high computation costs that result from extensive search or pre-training. In light of concerns with regard to the environment and the need for Green IT, we holistically analyze the computational cost of pipelines generated through various AutoML systems by combining the cost of system development, execution, and the downstream inference cost. Our findings show the benefits and disadvantages of implementation designs and their potential for Green AutoML.
