Sustainable CRISP-DM Extension for EnergyAware AI Development.

AI-based solutions show great potential in various fields, including the context of sustainability. In light of the great potential, it is often overlooked that advances in performance come at a significant cost to the environment, as training data- and computation-intensive models involves high carbon emissions. Climate change and its increasing awareness are forcing companies to use available resources more efficiently, which for the field of AI means developing accurate models in an energy-aware manner. We conduct a systematic literature review on approaches for sustainable AI development and organize the existing knowledge along the phases of the established CRISP-DM model. In this way, we provide managers and developers with a holistic picture of opportunities for reducing the environmental footprint in all phases of typical enterprise AI projects. [ABSTRACT FROM AUTHOR]