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Agility in Software 2.0 – Notebook Interfaces and MLOps with Buttresses and Rebars

Borg, Markus LU (2022) 6th International Conference on Lean and Agile Software Development, LASD 2022 In Lecture Notes in Business Information Processing 438 LNBIP. p.3-16
Abstract

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... (More)

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.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Lean and Agile Software Development - 6th International Conference, LASD 2022, Proceedings
series title
Lecture Notes in Business Information Processing
editor
Przybylek, Adam ; Jarzebowicz, Aleksander ; Lukovic, Ivan and Ng, Yen Ying
volume
438 LNBIP
pages
14 pages
publisher
Springer Science and Business Media B.V.
conference name
6th International Conference on Lean and Agile Software Development, LASD 2022
conference location
Virtual, Online
conference dates
2022-01-22 - 2022-01-22
external identifiers
  • scopus:85123981910
ISSN
1865-1348
1865-1356
ISBN
9783030942373
DOI
10.1007/978-3-030-94238-0_1
language
English
LU publication?
yes
additional info
Funding 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.? Funding Information: Agility Supported by Notebook Interfaces Publisher Copyright: © 2022, Springer Nature Switzerland AG.
id
a0f9f3e0-ca46-41f0-8db8-04af550eefcb
date added to LUP
2022-04-06 12:55:25
date last changed
2024-09-12 08:24:45
@inproceedings{a0f9f3e0-ca46-41f0-8db8-04af550eefcb,
  abstract     = {{<p>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.</p>}},
  author       = {{Borg, Markus}},
  booktitle    = {{Lean and Agile Software Development - 6th International Conference, LASD 2022, Proceedings}},
  editor       = {{Przybylek, Adam and Jarzebowicz, Aleksander and Lukovic, Ivan and Ng, Yen Ying}},
  isbn         = {{9783030942373}},
  issn         = {{1865-1348}},
  language     = {{eng}},
  pages        = {{3--16}},
  publisher    = {{Springer Science and Business Media B.V.}},
  series       = {{Lecture Notes in Business Information Processing}},
  title        = {{Agility in Software 2.0 – Notebook Interfaces and MLOps with Buttresses and Rebars}},
  url          = {{http://dx.doi.org/10.1007/978-3-030-94238-0_1}},
  doi          = {{10.1007/978-3-030-94238-0_1}},
  volume       = {{438 LNBIP}},
  year         = {{2022}},
}