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...
| Main Authors: | , , , , , , , , , , , , , , |
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| 格式: | Journal article |
| 语言: | English |
| 出版: |
Springer Nature
2022
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| _version_ | 1826311406501232640 |
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| author | Lavin, A Gilligan-Lee, CM Visnjic, A Ganju, S Newman, D Ganguly, S Lange, D Baydin, AG Baydin, AG Sharma, A Gibson, A Zheng, S Xing, EP Mattmann, C Parr, J Gal, Y |
| author_facet | Lavin, A Gilligan-Lee, CM Visnjic, A Ganju, S Newman, D Ganguly, S Lange, D Baydin, AG Baydin, AG Sharma, A Gibson, A Zheng, S Xing, EP Mattmann, C Parr, J Gal, Y |
| author_sort | Lavin, A |
| collection | OXFORD |
| description | 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 <i>Machine Learning Technology Readiness Levels</i> 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. |
| first_indexed | 2024-03-07T08:07:57Z |
| format | Journal article |
| id | oxford-uuid:4b2cc3be-a739-423e-bb2e-36c9e31084bb |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T08:07:57Z |
| publishDate | 2022 |
| publisher | Springer Nature |
| record_format | dspace |
| spelling | oxford-uuid:4b2cc3be-a739-423e-bb2e-36c9e31084bb2023-11-14T08:24:49ZTechnology readiness levels for machine learning systemsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:4b2cc3be-a739-423e-bb2e-36c9e31084bbEnglishSymplectic ElementsSpringer Nature2022Lavin, AGilligan-Lee, CMVisnjic, AGanju, SNewman, DGanguly, SLange, DBaydin, AGBaydin, AGSharma, AGibson, AZheng, SXing, EPMattmann, CParr, JGal, YThe 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 <i>Machine Learning Technology Readiness Levels</i> 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. |
| spellingShingle | Lavin, A Gilligan-Lee, CM Visnjic, A Ganju, S Newman, D Ganguly, S Lange, D Baydin, AG Baydin, AG Sharma, A Gibson, A Zheng, S Xing, EP Mattmann, C Parr, J Gal, Y Technology readiness levels for machine learning systems |
| title | Technology readiness levels for machine learning systems |
| title_full | Technology readiness levels for machine learning systems |
| title_fullStr | Technology readiness levels for machine learning systems |
| title_full_unstemmed | Technology readiness levels for machine learning systems |
| title_short | Technology readiness levels for machine learning systems |
| title_sort | technology readiness levels for machine learning systems |
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