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In order to try to solve this issue, NannyML was created. NannyML is an open-source Python library designed in order to make it easy to monitor drift in the distributions of our model input variables and estimate our model performance (even without labels!) thanks to the Confidence-Based Performance Estimation algorithm they developed. But first of all, why do models need to be monitored and why their performance might vary over time?
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Related posts
- Detecting silent model failure. NannyML estimates performance for regression and classification models using tabular data. It alerts you when and why it changed. It is the only open-source library capable of fully capturing the impact of data drift on performance.
- [D] Data drift is not a good indicator of model performance degradation
- [HIRING][Full Time, Part Time, Temporary, Internship, Freelance] Data Science Intern (Remote)
- What do you think about Detecting Silent ML Failure with an Open Source Python library?
- Can I estimate the impact of data drift on performance?