Model Drift
What is Model Drift?
In the artificial intelligence industry, model drift is a significant concern because it can lead to inaccurate predictions and suboptimal decisions. As real-world data evolves, the patterns and relationships that a model has learned may no longer be relevant. This misalignment can occur for various reasons, such as changes in user behavior, market conditions, or environmental factors. Monitoring and managing model drift involves regularly updating the model with new data, retraining it, and validating its performance to ensure it remains accurate and reliable. Ignoring model drift can render a once-effective model obsolete, leading to decreased trust in AI systems and potentially costly errors.
Model drift refers to the degradation of a machine learning model's performance over time due to changes in the underlying data patterns.
Examples
- A recommendation system for an e-commerce platform starts suggesting irrelevant products because customer preferences have shifted due to seasonal trends or new product launches.
- A credit scoring model becomes less accurate in assessing loan applications as economic conditions change, impacting variables like employment rates and average incomes.
Additional Information
- Model drift is also known as concept drift.
- Addressing model drift often requires a combination of monitoring tools, periodic retraining, and sometimes even redesigning the model.