Model Drift
Gradual degradation of AI model performance over time as real-world data patterns change from original training conditions.
Definition
Model drift occurs when the statistical properties of input data change over time, causing AI models to become less accurate. This happens because models are trained on historical data that may not reflect current conditions.
There are two main types: data drift (input patterns change) and concept drift (relationships between inputs and outputs change). Both require ongoing monitoring and model updates to maintain performance.
Why It Matters
Without addressing model drift, businesses risk making decisions based on increasingly inaccurate AI predictions. This can lead to lost revenue, poor customer experiences, and competitive disadvantages.
Proactive drift detection and model retraining strategies are essential for maintaining AI system reliability and ensuring continued return on AI investments across dynamic business environments.
Examples in Practice
E-commerce recommendation engines experience drift as customer preferences shift seasonally or due to cultural trends, requiring regular model updates to maintain relevance.
Credit scoring models drift as economic conditions change, potentially leading to inappropriate loan approvals or rejections if not continuously recalibrated.
Supply chain optimization models face drift during global events like pandemics, where historical shipping patterns become obsolete and new routing algorithms are needed.