| Autor: |
Alexander Lavin, Ciarán M. Gilligan-Lee, Alessya Visnjic, Siddha Ganju, Dava Newman, Sujoy Ganguly, Danny Lange, Atílím Güneş Baydin, Amit Sharma, Adam Gibson, Stephan Zheng, Eric P. Xing, Chris Mattmann, James Parr, Yarin Gal |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Nature Communications, Vol 13, Iss 1, Pp 1-19 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2041-1723 |
| DOI: |
10.1038/s41467-022-33128-9 |
| Popis: |
The development of machine learning systems has to ensure their robustness and reliability. The authors introduce a framework that defines a principled process of machine learning system formation, from research to production, for various domains and data scenarios. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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Full text from SpringerLink