Definition
Data drift occurs when the statistical properties of the data used to train an AI model change over time, impacting its performance. This shift can happen because the real world evolves, making the model's predictions less accurate. In practice, data drift means a model that once performed well may start making incorrect decisions or classifications. Understanding data drift is crucial for maintaining the reliability and effectiveness of AI systems in production environments. For instance, a spam filter trained on old email patterns might fail to catch new types of malicious messages. This is distinct from concept drift, where the relationship between input features and the target variable changes. Data drift is a key consideration in AI and machine learning domains tested on certifications like AWS Certified Machine Learning Specialty.