Efficient and Resilient Machine Learning for Industrial Applications
Philipp Wissmann
Philip Naumann
Daniel Hein
Steffen Udluft
Marc Weber
Simon Leszek
Thomas Runkler
Machine learning is rapidly transforming industrial landscapes, yet it faces significant hurdles related to efficiency and resilience. This paper discusses industrial challenges and provides a structured overview of current approaches, encompassing data-centric methodologies, efficient training for reliable solutions, hardware-optimized deployment, and the emerging role of foundation models.
