Even simply through a GoogleTrends search it becomes clear that Machine-Learning Operations-or MLOps, for short-are climbing in interest from both a scientific and practical perspective. On the one hand, software components and middleware are proliferating to support all manners of MLOps, from AutoML (i.e., software which enables developers with limited machine-learning expertise to train high-quality models specific to their domain or data) to feature-specific ML engineering, e.g., Explainability and Interpretability. On the other hand, the more these platforms penetrate the day-to-day activities of software operations, the more the risk for AI Software becoming unsustainable from a social, technical, or organisational perspective. This paper offers a concise definition of MLOps and AI Software Sustainability and outlines key challenges in its pursuit.
|Number of pages||7|
|Publication status||Published - Sep 2020|
Bibliographical noteDBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
- Machine-Learning Operations
- Software Sustainability