The integration of Machine Learning (ML) across public and industrial sectors has become widespread, posing unique challenges in comparison to conventional software development methods throughout the lifecycle of ML-Enabled Systems. Particularly, with the rising importance of ML platforms in software operations and the computational power associated with their frequent training, testing, and retraining, there is a growing concern about the sustainability of DevOps practices in the context of Al-enabled software. Despite the increasing interest in this domain, a comprehensive overview that offers a holistic perspective on research related to sustainable AI is currently lacking. This paper addresses this gap by presenting a Systematic Mapping Study that thoroughly examines techniques, tools, and lessons learned to assess and promote environmental sustainability in MLOps practices for ML-Enabled Systems.
