Summary of Maintainability Challenges in Ml: a Systematic Literature Review, by Karthik Shivashankar et al.
Maintainability Challenges in ML: A Systematic Literature Review
by Karthik Shivashankar, Antonio Martini
First submitted to arxiv on: 17 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Software Engineering (cs.SE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A systematic review of over 13,000 papers identified 56 studies that shed light on the maintainability challenges in different stages of Machine Learning (ML) workflows. The study aimed to understand how these stages are interdependent and impact each other’s maintainability. The results revealed a catalogue of 13 maintainability challenges across Data Engineering, Model Engineering, and current challenges when building ML systems. A map was created to illustrate the relationships between these challenges and their impact on the overall workflow. This research provides valuable insights for developers of ML tools and researchers, enabling them to build more maintainable ML systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning is getting better at doing many things, but it’s not very good at keeping itself working well over time. That’s a problem because it can make the technology worse than it was before. To fix this, we looked at what people have written about making machine learning work well in the long run. We found 56 studies that talked about different parts of how machine learning works and what makes them hard to keep working well. Now we know what the problems are, so we can start fixing them and make machine learning better for everyone. |
Keywords
* Artificial intelligence * Machine learning
