Term
Model Drift
Definition
Model drift is when the effectiveness or accuracy of a machine learning model lessens over time because the type of data it is working with has changed.
Where you’ll find it
Model drift is typically discussed in AI system maintenance and monitoring settings. It is relevant to any AI application that relies on ongoing data inputs, such as predictive analytics tools.
Common use cases
- Continuously monitoring AI performance to ensure accuracy.
- Adjusting AI models in response to new data trends.
- Regularly updating training sets to align with current data characteristics.
Things to watch out for
- Failing to detect and address model drift can lead to significantly poor model performance.
- Not all changes in data will cause model drift; determining the cause can sometimes be complex.
- Overfitting during model recalibration can occur if not carefully managed.
Related terms
- Data Distribution
- Predictive Analytics
- Machine Learning
- Training Dataset
- Monitoring Tools
Pixelhaze Tip: Keep a regular check-up routine for your AI models, just like you would service a vehicle. Simple diagnostic checks using monitoring tools can save you from major headaches down the road.