Description
Maintaining machine learning models in production can be quite different from maintaining general software projects, because of the unique statistical characteristics of ML models.
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; decisions made along the way can make the difference between resilient models that are easier to maintain and disappointing or misleading models.
Materials: https://github.com/juliasilge/ml-maintenance-2023