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- Model drift detection monitors production ML models for performance degradation caused by changes in input data or the relationship between inputs and outcomes<\/li>\n\n\n\n
- There are three main types of drift: data drift (input distribution changes), concept drift (the relationship between inputs and outputs changes), and prediction drift (model output distribution shifts)<\/li>\n\n\n\n
- Detecting drift requires comparing production data distributions and model performance metrics against a baseline established at deployment time<\/li>\n\n\n\n
- Statistical tests including the Kolmogorov-Smirnov test, Population Stability Index, and Jensen-Shannon divergence are the most common tools for detecting data drift<\/li>\n<\/ul>\n\n\n\n
The Three Types of Drift<\/strong><\/h2>\n\n\n\n
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- Data Drift<\/strong><\/li>\n<\/ol>\n\n\n\n
Data drift, also called covariate shift, occurs when the statistical distribution of input features changes after deployment. The model’s learned mapping from inputs to outputs may still be correct, but because it is now receiving different inputs than it was trained on, predictions become unreliable.<\/p>\n\n\n\n
Data drift can affect individual features or combinations of features. A feature that was normally distributed during training may become right-skewed in production. Two features that were highly correlated during training may decorrelate. A categorical feature may develop new category values that were absent from training data.<\/p>\n\n\n\n
Data drift is the most detectable type of drift because it can be identified without any ground truth labels, just by comparing input distributions between training and production data.<\/p>\n\n\n\n
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- Concept Drift<\/strong><\/li>\n<\/ol>\n\n\n\n
Concept drift occurs when the underlying relationship between inputs and outputs changes. Even if the input distribution remains stable, the correct output for a given input is now different from what the model learned.<\/p>\n\n\n\n
A sentiment analysis model trained on product reviews may develop concept drift if the vocabulary of sentiment expression changes over time. A price prediction model may experience concept drift during unusual economic conditions where historical input-output relationships break down.<\/p>\n\n\n\n
Concept drift is harder to detect because it requires observing actual outcomes, not just inputs. You need ground truth labels from production to measure it directly.<\/p>\n\n\n\n
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- Prediction Drift<\/strong><\/li>\n<\/ol>\n\n\n\n
Prediction drift <\/a>occurs when the distribution of the model’s output predictions shifts, even if individual predictions may still be correct. If a binary classifier that previously predicted class 1 for 30 percent of inputs suddenly predicts class 1 for 60 percent of inputs, something has changed.<\/p>\n\n\n\n
Prediction drift can be detected without ground truth and often serves as an early warning signal of data drift or concept drift upstream. Monitoring output distribution is a lightweight first line of defense.<\/p>\n\n\n\n
Want to build production MLOps skills covering model monitoring, drift detection, and automated retraining pipelines? Explore HCL GUVI’s <\/strong>Artificial Intelligence & Machine Learning Course<\/strong><\/a>, designed to help you develop the practical ML engineering foundations that real production AI roles demand. <\/a><\/p>\n\n\n\n
Drift Types at a Glance<\/strong><\/h2>\n\n\n\n
- Prediction Drift<\/strong><\/li>\n<\/ol>\n\n\n\n
- Concept Drift<\/strong><\/li>\n<\/ol>\n\n\n\n
- Data Drift<\/strong><\/li>\n<\/ol>\n\n\n\n
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