Journal article
Technology readiness levels for machine learning systems
- 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.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 2.1MB, Terms of use)
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- Publisher copy:
- 10.1038/s41467-022-33128-9
Authors
- Publisher:
- Springer Nature
- Journal:
- Nature Communications More from this journal
- Volume:
- 13
- Issue:
- 1
- Article number:
- 6039
- Publication date:
- 2022-10-20
- Acceptance date:
- 2022-09-02
- DOI:
- EISSN:
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2041-1723
- Pmid:
-
36266298
- Language:
-
English
- Keywords:
- Pubs id:
-
1286658
- Local pid:
-
pubs:1286658
- Deposit date:
-
2022-11-03
- ARK identifier:
Terms of use
- Copyright holder:
- Lavin et al.
- Copyright date:
- 2022
- Rights statement:
- © The Author(s) 2022. Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
- Licence:
- CC Attribution (CC BY)
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