Term
Data Drift (ˈdeɪ.tə drɪft)
Definition
Data drift refers to the changes over time in the way input data appears and behaves, which can negatively affect the performance of an AI model.
Where you’ll find it
Data drift can occur in any AI system that continuously receives and processes new data. This concept typically appears in monitoring tools or dashboards that are part of an AI or machine learning platform, helping users observe and control their model’s performance over time.
Common use cases
- Improving model reliability by identifying shifts in data patterns that could make the AI less accurate.
- Maintaining accuracy in predictions by adjusting models when input data changes.
- Ensuring consistency in automated decision-making processes by regularly updating data sets.
Things to watch out for
- It can be challenging to distinguish between data drift and other issues like model degradation; clear metrics are needed to identify it accurately.
- Not all changes in input data are harmful; identifying which changes matter is crucial.
- Regular updates and monitoring are required to effectively manage data drift.
Related terms
- Model Monitoring
- Machine Learning
- Prediction Accuracy
- Data Quality
- Feature Engineering
Pixelhaze Tip: Always pair data drift monitoring with regular model evaluations to ensure your AI continues to perform well as input data evolves. This proactive approach can save you from unexpected performance drops.