How is model drift handled?

Model drift refers to the decline in the accuracy of a machine learning model over time. Oracle detects and mitigates model drift by monitoring model performance metrics, comparing model outputs with ground truth (verified data), or human evaluation, and tracking evaluation metrics over time. To maintain performance, we also retrain models regularly with these practices:

• Updating the model with new data
• Reevaluating underlying assumptions
• Fine-tuning model parameters
• Applying transfer learning
• Adapting to evolving data patterns

Note: The exact processes vary according to product and feature.