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25 août 2020
- handle: 10670/1.vuzkqo
- hal: hal-03414730
- doi: 10.1007/978-3-030-57321-8_25
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info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-030-57321-8_25
http://creativecommons.org/licenses/by/ , info:eu-repo/semantics/OpenAccess
Mots-clés
Machine learning systems Data quality Domain adaptation Hybrid models Software engineering Embedded systems Human centered AISujets proches
Computer software engineering Engineering systems--Design and construction System engineering Natural science Sciences Science of science Learning, MachineCiter ce document
Lukas Fischer et al., « Applying AI in Practice: Key Challenges and Lessons Learned », HAL-SHS : sciences de l'information, de la communication et des bibliothèques, ID : 10.1007/978-3-030-57321-8_25
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Résumé
The main challenges along with lessons learned from ongoing research in the application of machine learning systems in practice are discussed, taking into account aspects of theoretical foundations, systems engineering, and human-centered AI postulates. 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.