AI System Engineering - Key Challenges and Lessons Learned

L. Fischer, L. Ehrlinger, V. Geist, R. Ramler, F. Sobiezky, W. Zellinger, D. Brunner, M. Kumar, B. Moser. AI System Engineering - Key Challenges and Lessons Learned. Machine Learning & Knowledge Extraction, volume 3, number 1, pages 56-83, DOI https://doi.org/10.3390/make3010004, 12, 2020.

Autoren
  • Lukas Fischer
  • Lisa Ehrlinger
  • Verena Geist
  • Rudolf Ramler
  • Florian Sobiezky
  • Werner Zellinger
  • David Brunner
  • Mohit Kumar
  • Bernhard A. Moser
TypArtikel
JournalMachine Learning & Knowledge Extraction
Nummer1
Band3
DOIhttps://doi.org/10.3390/make3010004
Monat12
Jahr2020
Seiten56-83
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.