Standard 3 stops to get here · leads to 1
Data Drift
Changes in input data distribution over time that can degrade model performance in production.
Your route here
3 stops · basics first
- Dataset ✓ understood
A collection of data examples used for training, validating, or testing machine learning models.
- Training ✓ understood
The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.
- Inference ✓ understood
Running a trained model on new inputs to get predictions, with its weights frozen: the stage of a model's life that users actually interact with.
- Data Drift · you are here ✓ understood
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Shipping AI Model Drift Degradation of model performance over time due to changes in the relationship between features and target. Shipping AI Model Monitoring Tracking model performance, data distribution, and predictions in production to detect issues and degradation. Evaluation Out-of-Distribution Data that differs significantly from the training distribution, where models often perform poorly or unreliably. Shipping AI Model Retraining Periodically updating models with new data to maintain performance as distributions change. Shipping AI Model Performance Degradation Decline in model quality over time due to distribution shift or changing patterns.