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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...
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 |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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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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