Summary of Experimentation, Deployment and Monitoring Machine Learning Models: Approaches For Applying Mlops, by Diego Nogare et al.
Experimentation, deployment and monitoring Machine Learning models: Approaches for applying MLOps
by Diego Nogare, Ismar Frango Silveira
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The abstract discusses the growing importance of data science in industry decision-making, as well as the emergence of MLOps (Machine Learning Operations) as a solution for automating the machine learning model life cycle. The discipline involves integrating development and production environments, publishing models, and monitoring them throughout the lifecycle. Research results highlight the evolving nature of MLOps, its challenges, and solutions. This paper contributes to the understanding of various MLOps techniques and their diverse applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MLOps helps companies use machine learning models more efficiently by automating tasks like testing and deployment. The goal is to make it easier for data scientists to develop and use these models in real-world situations. The field is constantly changing as new challenges arise, but researchers are working to solve these problems and make MLOps a reliable tool. |
Keywords
» Artificial intelligence » Machine learning
