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AI System Engineering-Key Challenges and Lessons Learned
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DOI：10.3390/make3010004.png)
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摘要
En 中文
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.
Keyword:
AI system engineering
deep learning
embedded AI
federated learning
transfer learning
human centered AI
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对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
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6
论文数:
849
被引数:
1.8K
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