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AI System Engineering—Key Challenges and Lessons Learned
| Content Provider | MDPI |
|---|---|
| Author | Fischer, Lukas Ehrlinger, Lisa Geist, Verena Ramler, Rudolf Sobiezky, Florian Zellinger, Werner Brunner, David Kumar, Mohit Moser, Bernhard |
| Copyright Year | 2020 |
| Description | 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. |
| Ending Page | 83 |
| Page Count | 28 |
| Starting Page | 56 |
| e-ISSN | 25044990 |
| DOI | 10.3390/make3010004 |
| Journal | Machine Learning and Knowledge Extraction |
| Issue Number | 1 |
| Volume Number | 3 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-12-31 |
| Access Restriction | Open |
| Subject Keyword | Machine Learning and Knowledge Extraction Information Systems Ai System Engineering Deep Learning Embedded Ai Federated Learning Transfer Learning Human Centered Ai |
| Content Type | Text |
| Resource Type | Article |
