Technology readiness levels for machine learning systems
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.
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-10-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-022-33128-9 |
| _version_ | 1811249985764720640 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Alexander Lavin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T15:57:05Z |
| format | Article |
| id | doaj.art-5d44c96b0792477fb2fa35f35c38a58a |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-04-12T15:57:05Z |
| publishDate | 2022-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-5d44c96b0792477fb2fa35f35c38a58a2022-12-22T03:26:20ZengNature PortfolioNature Communications2041-17232022-10-0113111910.1038/s41467-022-33128-9Technology readiness levels for machine learning systemsAlexander Lavin0Ciarán M. Gilligan-Lee1Alessya Visnjic2Siddha Ganju3Dava Newman4Sujoy Ganguly5Danny Lange6Atílím Güneş Baydin7Amit Sharma8Adam Gibson9Stephan Zheng10Eric P. Xing11Chris Mattmann12James Parr13Yarin Gal14Pasteur Labs & ISISpotifyWhyLabsNASA Frontier Development LabMassachusetts Institute of TechnologyUnity AIUnity AIUniversity of OxfordMicrosoft ResearchKonduitSalesforce ResearchPetuumNASA Jet Propulsion LabNASA Frontier Development LabAlan Turing InstituteThe 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.https://doi.org/10.1038/s41467-022-33128-9 |
| spellingShingle | 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 Technology readiness levels for machine learning systems Nature Communications |
| title | Technology readiness levels for machine learning systems |
| title_full | Technology readiness levels for machine learning systems |
| title_fullStr | Technology readiness levels for machine learning systems |
| title_full_unstemmed | Technology readiness levels for machine learning systems |
| title_short | Technology readiness levels for machine learning systems |
| title_sort | technology readiness levels for machine learning systems |
| url | https://doi.org/10.1038/s41467-022-33128-9 |
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