Data Drift
Changes in the statistical properties of input data over time that can degrade machine learning model performance.
In-Depth Explanation
Data drift occurs when the statistical properties of model input data change over time, potentially degrading model performance. It's a key concern for deployed ML models.
Types of drift:
- Covariate drift: Input distribution changes
- Prior drift: Target distribution changes
- Concept drift: Input-output relationship changes
Causes of drift:
- Seasonality and trends
- Business changes
- User behaviour shifts
- External events
- Data collection changes
Detection methods:
- Statistical tests (KS test, chi-square)
- Distribution comparison
- Feature monitoring
- Prediction monitoring
- Performance degradation alerts
Handling drift:
- Regular model retraining
- Online learning
- Drift-aware models
- Human-in-the-loop review
Business Context
Deployed models can silently degrade as data changes. Drift detection is essential for maintaining production model reliability.
How Clever Ops Uses This
We implement drift monitoring for Australian business ML deployments, ensuring models remain accurate as business conditions change.
Example Use Case
"Detecting that a customer churn model is receiving different customer demographics than it was trained on, triggering investigation and potential retraining."
Frequently Asked Questions
Related Terms
Related Resources
Concept Drift
Changes in the relationship between input features and target variables over tim...
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