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Technology readiness levels for machine learning systems

The development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. Lack of diligence can lead to technical debt, scope creep and misaligned objectives, model misuse and failures, and expensive consequences. En...

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Autores principales: Lavin, Alexander, Gilligan-Lee, Ciarán M., Visnjic, Alessya, Ganju, Siddha, Newman, Dava, Ganguly, Sujoy, Lange, Danny, Baydin, Atílím Güneş, Sharma, Amit, Gibson, Adam, Zheng, Stephan, Xing, Eric P., Mattmann, Chris, Parr, James, Gal, Yarin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9585100/
https://www.ncbi.nlm.nih.gov/pubmed/36266298
http://dx.doi.org/10.1038/s41467-022-33128-9
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author Lavin, Alexander
Gilligan-Lee, Ciarán M.
Visnjic, Alessya
Ganju, Siddha
Newman, Dava
Ganguly, Sujoy
Lange, Danny
Baydin, Atílím Güneş
Sharma, Amit
Gibson, Adam
Zheng, Stephan
Xing, Eric P.
Mattmann, Chris
Parr, James
Gal, Yarin
author_facet Lavin, Alexander
Gilligan-Lee, Ciarán M.
Visnjic, Alessya
Ganju, Siddha
Newman, Dava
Ganguly, Sujoy
Lange, Danny
Baydin, Atílím Güneş
Sharma, Amit
Gibson, Adam
Zheng, Stephan
Xing, Eric P.
Mattmann, Chris
Parr, James
Gal, Yarin
author_sort Lavin, Alexander
collection PubMed
description The development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. Lack of diligence can lead to technical debt, scope creep and misaligned objectives, model misuse and failures, and expensive consequences. Engineering systems, on the other hand, follow well-defined processes and testing standards to streamline development for high-quality, reliable results. The extreme is spacecraft systems, with mission critical measures and robustness throughout the process. Drawing on experience in both spacecraft engineering and machine learning (research through product across domain areas), we’ve developed a proven systems engineering approach for machine learning and artificial intelligence: the Machine Learning Technology Readiness Levels framework defines a principled process to ensure robust, reliable, and responsible systems while being streamlined for machine learning workflows, including key distinctions from traditional software engineering, and a lingua franca for people across teams and organizations to work collaboratively on machine learning and artificial intelligence technologies. Here we describe the framework and elucidate with use-cases from physics research to computer vision apps to medical diagnostics.
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spelling pubmed-95851002022-10-22 Technology readiness levels for machine learning systems Lavin, Alexander Gilligan-Lee, Ciarán M. Visnjic, Alessya Ganju, Siddha Newman, Dava Ganguly, Sujoy Lange, Danny Baydin, Atílím Güneş Sharma, Amit Gibson, Adam Zheng, Stephan Xing, Eric P. Mattmann, Chris Parr, James Gal, Yarin Nat Commun Article The development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. Lack of diligence can lead to technical debt, scope creep and misaligned objectives, model misuse and failures, and expensive consequences. Engineering systems, on the other hand, follow well-defined processes and testing standards to streamline development for high-quality, reliable results. The extreme is spacecraft systems, with mission critical measures and robustness throughout the process. Drawing on experience in both spacecraft engineering and machine learning (research through product across domain areas), we’ve developed a proven systems engineering approach for machine learning and artificial intelligence: the Machine Learning Technology Readiness Levels framework defines a principled process to ensure robust, reliable, and responsible systems while being streamlined for machine learning workflows, including key distinctions from traditional software engineering, and a lingua franca for people across teams and organizations to work collaboratively on machine learning and artificial intelligence technologies. Here we describe the framework and elucidate with use-cases from physics research to computer vision apps to medical diagnostics. Nature Publishing Group UK 2022-10-20 /pmc/articles/PMC9585100/ /pubmed/36266298 http://dx.doi.org/10.1038/s41467-022-33128-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Lavin, Alexander
Gilligan-Lee, Ciarán M.
Visnjic, Alessya
Ganju, Siddha
Newman, Dava
Ganguly, Sujoy
Lange, Danny
Baydin, Atílím Güneş
Sharma, Amit
Gibson, Adam
Zheng, Stephan
Xing, Eric P.
Mattmann, Chris
Parr, James
Gal, Yarin
Technology readiness levels for machine learning systems
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
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9585100/
https://www.ncbi.nlm.nih.gov/pubmed/36266298
http://dx.doi.org/10.1038/s41467-022-33128-9
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