Monitoring and mitigating drift in ML models
All three types of drift (model, data, and concept) are important to measure, as they impact the performance and accuracy of ML models. Monitoring and mitigating each type of drift is essential to maintain model performance over time:
- As discussed, model drift occurs when a model’s performance degrades as it becomes outdated due to changes in the underlying data distribution or concept.
Mitigation: Regularly retrain the model with fresh, representative data to maintain its performance. For example, retrain a sales prediction model with new sales data to capture recent trends and changes in customer behavior.
- Data drift occurs when the input data distribution changes over time, making the model’s training data less representative of the current data.
Mitigation: Continuously monitor the distribution of input features and compare them to the training data. If significant deviations are detected,...
