Sustainable MLOps: Trends and Challenges from Practice
Damian Andrew Tamburri
Associate Professor
DEIB - Alpha Room (Building 24)
December 13th, 2022
2.30 pm
Contacts:
Damian Andrew Tamburri
Research Line:
Advanced software architectures and methodologies
Associate Professor
DEIB - Alpha Room (Building 24)
December 13th, 2022
2.30 pm
Contacts:
Damian Andrew Tamburri
Research Line:
Advanced software architectures and methodologies
Abstract
On December 13th, 2022 at 2.30 pm Damian Andrew Tamburri, DEIB Associate Professor, will hold a seminar on "Sustainable MLOps: Trends and Challenges from Practice" in DEIB - Alpha Room.
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 talk offers a concise definition of MLOps and AI Software Sustainability and outlines key challenges in its pursuit.
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 talk offers a concise definition of MLOps and AI Software Sustainability and outlines key challenges in its pursuit.
