A Taxonomy of ML for Systems Problems
Abstract
Machine learning has the potential to significantly improve systems, but only under certain conditions. We describe a taxonomy to help identify whether or not machine learning should be applied to particular systems problems, and which approaches are most promising. We believe that this taxonomy can help practitioners and researchers decide how to most effectively use machine learning in their systems, and provide the community with a framework and vocabulary to discuss different approaches for applying machine learning in systems.
Research Areas
