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[gorkemgiray]

Short description: The paper includes the challenges and the proposed solutions for engineering machine learning (ML) systems from a software engineering (SE) perspective. The source of this analysis is 141 papers published in SE venues.
Why is it relevant? The paper is on SE aspects of engineering ML systems.
What is the lesson learned from reading the article? All challenges categorized and listed under SE subareas, i.e., requirements engineering, design, etc. The non-deterministic nature of ML systems complicates all SE aspects of engineering ML systems. Despite increasing interest from 2018 onwards, the results reveal that none of the SE aspects have a mature set of tools and techniques. Testing is by far the most popular area among researchers. Even for testing ML systems, engineers have only some tool prototypes and solution proposals with weak experimental proof. Many of the challenges of ML systems engineering were identified through surveys and interviews. Researchers should conduct experiments and case studies, ideally in industrial environments, to further understand these challenges and propose solutions.
How does it add new insights over the articles already listed? It involves a high-level and broad SE view for engineering ML systems.
P.S.: I added the paper at the top of the list since you want to have the lists in an alphabetical order. Please feel free to change its order if you accept the paper to include in the list.

[xserban]

Thanks for your contribution! I find the study relevant, although it focuses more on uncovering challenges and offers less details on how to solve the challenges. It would be interesting to discuss if the solutions presented in literature and outlined in the study are sufficient and timely.

A more open question, do you think any of the practical solutions are worth adding to this catalogue: https://se-ml.github.io/?