Artificial intelligence through machine learning is increasingly used in the digital society. Solutions based on machine learning bring both great opportunities, thus coined “Software 2.0,” but also great challenges for the engineering community to tackle. Due to the experimental approach used by data scientists when developing machine learning models, agility is an essential characteristic. In this keynote address, we discuss two contemporary development phenomena that are fundamental in machine learning development, i.e., notebook interfaces and MLOps. First, we present a solution that can remedy some of the intrinsic weaknesses of working in notebooks by supporting easy transitions to integrated development environments. Second, we propose reinforced engineering of AI systems by introducing metaphorical buttresses and rebars in the MLOps context. Machine learning-based solutions are dynamic in nature, and we argue that reinforced continuous engineering is required to quality assure the trustworthy AI systems of tomorrow.
|Titel på värdpublikation||Lean and Agile Software Development - 6th International Conference, LASD 2022, Proceedings|
|Redaktörer||Adam Przybylek, Aleksander Jarzebowicz, Ivan Lukovic, Yen Ying Ng|
|Förlag||Springer Science and Business Media B.V.|
|Status||Published - 2022|
|Evenemang||6th International Conference on Lean and Agile Software Development, LASD 2022 - Virtual, Online|
Varaktighet: 2022 jan. 22 → 2022 jan. 22
|Namn||Lecture Notes in Business Information Processing|
|Konferens||6th International Conference on Lean and Agile Software Development, LASD 2022|
|Period||2022/01/22 → 2022/01/22|
Bibliografisk informationFunding Information:
Martin Jakobsson and Johan Henriksson are the co-creators of the solution presented in Sect. 2 and deserve all credit for this work. Our thanks go to Backtick Technologies for hosting the MSc thesis project and Dr. Niklas Fors, Dept. of Computer Science, Lund University for acting as the examiner. This initiative received financial support through the AIQ Meta-Testbed project funded by Kompetensfonden at Campus Helsingborg, Lund University, Sweden and two internal RISE initiatives, i.e., ?SODA-Software & Data Intensive Applications? and ?MLOps by RISE.?
Agility Supported by Notebook Interfaces
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