@inproceedings{tamburri_sustainable_2020,
	title = {Sustainable {MLOps}: {Trends} and {Challenges}},
	shorttitle = {Sustainable {MLOps}},
	doi = {10.1109/SYNASC51798.2020.00015},
	abstract = {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.},
	booktitle = {2020 22nd {International} {Symposium} on {Symbolic} and {Numeric} {Algorithms} for {Scientific} {Computing} ({SYNASC})},
	author = {Tamburri, Damian A.},
	month = sep,
	year = {2020},
	keywords = {DataOps, Decision making, MLOps, Machine learning, Machine-Learning Operations, Market research, Middleware, Scientific computing, Software Sustainability, Software systems, Sustainable development},
	pages = {17--23},
}

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