Technology readiness levels for machine learning systems

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 and Yarin Gal
Additional contact information
Alexander Lavin: Pasteur Labs & ISI
Ciarán M. Gilligan-Lee: Spotify
Alessya Visnjic: WhyLabs
Siddha Ganju: NASA Frontier Development Lab
Dava Newman: Massachusetts Institute of Technology
Sujoy Ganguly: Unity AI
Danny Lange: Unity AI
Atílím Güneş Baydin: University of Oxford
Amit Sharma: Microsoft Research
Adam Gibson: Konduit
Stephan Zheng: Salesforce Research
Eric P. Xing: Petuum
Chris Mattmann: NASA Jet Propulsion Lab
James Parr: NASA Frontier Development Lab
Yarin Gal: Alan Turing Institute

Nature Communications, 2022, vol. 13, issue 1, 1-19

Abstract: Abstract 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.

Date: 2022
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DOI: 10.1038/s41467-022-33128-9

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