The Environmental Cost of Engineering Machine Learning-Enabled Systems: A Mapping Study

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.