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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Bibliografische gegevens
Hoofdauteurs:	Lavin, A, Gilligan-Lee, CM, Visnjic, A, Ganju, S, Newman, D, Ganguly, S, Lange, D, Baydin, AG, Sharma, A, Gibson, A, Zheng, S, Xing, EP, Mattmann, C, Parr, J, Gal, Y
Formaat:	Journal article
Taal:	English
Gepubliceerd in:	Springer Nature 2022

Technology readiness levels for machine learning systems door Lavin, A, Gilligan-Lee, CM, Visnjic, A, Ganju, S, Newman, D, Baydin, AG, Ganguly, S, Lange, D, Sharma, A, Zheng, S, Xing, EP, Gibson, A, Parr, J, Mattmann, C, Gal, Y

Gepubliceerd in 2021

Internet publication

Technology readiness levels for machine learning systems

Technology readiness levels for machine learning systems door Lavin, A, Gilligan-Lee, CM, Visnjic, A, Ganju, S, Newman, D, Baydin, AG, Ganguly, S, Lange, D, Sharma, A, Zheng, S, Xing, EP, Gibson, A, Parr, J, Mattmann, C, Gal, Y

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