Name
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/make3010004 | MACHINE LEARNING AND KNOWLEDGE EXTRACTION |
Keywords | DocType | Volume |
AI system engineering, deep learning, embedded AI, federated learning, transfer learning, human centered AI | Journal | 3 |
Issue | Citations | PageRank |
1 | 0 | 0.34 |
References | Authors | |
0 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lukas Fischer | 1 | 0 | 0.34 |
| Lisa Ehrlinger | 2 | 0 | 1.01 |
| Verena Geist | 3 | 0 | 0.34 |
| Rudolf Ramler | 4 | 0 | 0.34 |
| Florian Sobiezky | 5 | 0 | 0.34 |
| Werner Zellinger | 6 | 32 | 4.27 |
| David Brunner | 7 | 0 | 1.01 |
| Mohit Kumar | 8 | 182 | 17.42 |
| Bernhard Moser | 9 | 0 | 0.34 |