Technology readiness levels for machine learning systems

• Published in: Nature communications Vol. 13; no. 1; p. 6039
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 20.10.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/984; 639/705/1042; 639/705/258; 639/705/794; 706/703/559; Artificial Intelligence; Computer vision; Domains; Engineering; Extreme values; Failure analysis; Humanities and Social Sciences; Humans; Learning algorithms; Machine Learning; Medical research; multidisciplinary; R&D; Research & development; Robustness; Science; Science (multidisciplinary); Software; Software engineering; Spacecraft; Systems engineering; Technology; Technology assessment; Technology utilization
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-022-33128-9

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. The development of machine learning systems has to ensure their robustness and reliability. The authors introduce a framework that defines a principled process of machine learning system formation, from research to production, for various domains and data scenarios.