AI System Engineering - Key Challenges and Lessons Learned

Authors Lukas Fischer
Lisa Ehrlinger
Verena Geist
Rudolf Ramler
Florian Sobiezky
Werner Zellinger
David Brunner
Mohit Kumar
Bernhard A. Moser
Editors
Title AI System Engineering - Key Challenges and Lessons Learned
Type article
Journal Machine Learning & Knowledge Extraction
Number 1
Volume 3
DOI 10.3390/make3010004
Month December
Year 2020
Pages 56-83
SCCH ID# 21001
Abstract

The main challenges are discussed together with the lessons learned from past and ongoing research along the development cycle of machine learning systems. This will be done by taking into account intrinsic conditions of nowadays deep learning models, data and software quality issues and human-centered artificial intelligence (AI) postulates, including confidentiality and ethical aspects. The analysis outlines a fundamental theory-practice gap which superimposes the challenges of AI system engineering at the level of data quality assurance, model building, software engineering and deployment. The aim of this paper is to pinpoint research topics to explore approaches to address these challenges.