https://github.com/juliasilge/ml-maintenance-2023

Talk for posit::conf() 2023 on reliable maintenance of machine learning models
https://github.com/juliasilge/ml-maintenance-2023

Last synced: about 1 month ago
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Talk for posit::conf() 2023 on reliable maintenance of machine learning models

• Host: GitHub
• URL: https://github.com/juliasilge/ml-maintenance-2023
• Owner: juliasilge
• Created: 2023-08-10T02:10:09.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-11-19T16:38:48.000Z (about 1 month ago)
• Last Synced: 2024-11-19T17:42:04.570Z (about 1 month ago)
• Language: CSS
• Homepage: https://juliasilge.github.io/ml-maintenance-2023/
• Size: 11.5 MB
• Stars: 6
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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# Reliable maintenance of machine learning models

Slides for my talk for [posit::conf(2023)](https://pos.it/conf/)

![GIPHY](https://media.giphy.com/media/3oD3YGIVrRGbe5YGNa/giphy.gif)

Maintaining machine learning models in production can be quite different from maintaining general software engineering projects, each with different challenges and common failure modes. In this talk, learn about model drift, the different ways the word “performance” is used with models, what you can monitor about a model, how feedback loops impact models, and how you can use vetiver to set yourself up for success with model maintenance. This talk will help practitioners who are already deploying models, but this is also useful knowledge for practitioners earlier in their MLOps journey, because decisions made along the way can make the difference between resilient models that are easier to maintain and disappointing or misleading models.

> [!NOTE]  

> Watch [the recording on YouTube](https://youtu.be/LGXi2R70pVc)

Slides created with [Quarto](https://quarto.org/)