mlsanityData to 7 Oct 2026

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

arXiv 2111.14142 ↗First posted 28 Nov 2021cs.SE cs.AIcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 11 of its 47 references that are in the map. First posted on arXiv 28 Nov 2021.

Builds on

11 of 47 references.

References · 11

  1. 2018Explaining Explanations: An Overview of Interpretability of Machine Learningcited by 508
  2. 2020Underspecification Presents Challenges for Credibility in Modern Machine Learningcited by 488
  3. 2019Machine Learning Testing: Survey, Landscapes and Horizonscited by 213
  4. 2019Requirements Engineering for Machine Learning: Perspectives from Data Scientistscited by 49
  5. 2018Safely Entering the Deep: A Review of Verification and Validation for Machine Learning and a Challenge Elicitation in the Automotive Industrycited by 38
  6. 2020Engineering AI Systems: A Research Agendacited by 37
  7. 2020A Software Engineering Perspective on Engineering Machine Learning Systems: State of the Art and Challengescited by 33
  8. 2018Verification for Machine Learning, Autonomy, and Neural Networks Surveycited by 24
  9. 2021Non-functional Requirements for Machine Learning: Understanding Current Use and Challenges in Industrycited by 9
  10. 2021Certification of embedded systems based on Machine Learning: A surveycited by 6
  11. 2021Test Automation with Grad-CAM Heatmaps -- A Future Pipe Segment in MLOps for Vision AI?cited by 3

Built on by

Cited by year

Citing papers per year: 2022: 3; 2023: 1.

4 papers.

  1. 2023MLOps: A Reviewcited by 16
  2. 2022Exploring ML testing in practice -- Lessons learned from an interactive rapid review with Axis Communicationscited by 3
  3. 2022Quality Assurance of Generative Dialog Models in an Evolving Conversational Agent Used for Swedish Language Practicecited by 0
  4. 2022A Systematic Literature Review of Soft Computing Techniques for Software Maintainability Prediction: State-of-the-Art, Challenges and Future Directionscited by 0