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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Detalles Bibliográficos
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:
Article
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

Internet

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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