What is data drift detection?

Data drift detection refers to the process of identifying the statistical properties in the input data over time of a machine learning model. Common methods for monitoring include statistical tests and metrics like population stability index and z-scores which help in quantifying how much data has changed.

What is data drift?

Data drift refers to changes in the statistical properties of input data that a machine learning model received after the model has been deployed. This shift can lead to a decline in the model’s performance because the data it encounters in real time differs from what it has been trained on.

Data drift can occur due to various reasons, including changes in real-world conditions and errors in data collection. Data drift monitoring is essential for ensuring the reliability of AI models. By actively monitoring data drift, organizations can maintain model performance and ethical AI use in changing environments.  

Types of data drift:

• Feature drift: When the input variables (features) change over time.
• Target drift: When the output variable changes.
• Concept drift: When there are changes in the relationship between input features and target variables.