Summary of Towards Trustworthy Machine Learning in Production: An Overview Of the Robustness in Mlops Approach, by Firas Bayram et al.
Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approach
by Firas Bayram, Bestoun S. Ahmed
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Machine learning educators can summarize the abstract by stating that the research paper provides a comprehensive overview of the trustworthiness property of Machine Learning Operations (MLOps) systems. The authors highlight technical practices for achieving robust MLOps systems and survey existing research approaches addressing ML system robustness in production environments. Additionally, they review available tools and software supporting the development of MLOps systems to handle robustness aspects. Finally, the paper presents open challenges and proposes future directions within this emerging field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure artificial intelligence systems work well in real-life situations. Right now, AI systems are trained on past data and then used to make decisions. But what happens when these systems need to keep learning and adapting as new information becomes available? To address this challenge, a new field called Machine Learning Operations (MLOps) has emerged. MLOps helps standardize how AI solutions work in the real world. This paper takes a closer look at MLOps and explores ways to make sure these systems are trustworthy and reliable. |
Keywords
* Artificial intelligence * Machine learning
