Model drift (or model drift "This is a phenomenon where the performance of an AI model degrades over time, requiring adjustments or updating.
Ce phénomène se produit lorsque les performances d’un modèle d’machine learning diminuent avec le temps en raison de changements dans les données d’entrée ou dans l’environnement dans lequel le modèle est déployé.
In other words, a model may have been trained on a specific set of data and worked well in a certain context, but when confronted with new data or a different environment, its predictions may become less accurate or even wrong.
Causes of model drift
Several factors can contribute to model drift, including :
- Data evolution The data on which the model has been trained may no longer be representative of the real data encountered in production. The distributions of variables, the relationships between them, or even the nature of the data may change over time.
- Change of environment The environment in which the model is deployed may change, which may affect the input data or the way in which the model is used.
- Data quality problems Errors or inconsistencies in the new data can also cause the model to drift.
💥 Consequences of drifting
Model drift can have major consequences, including :
- Reduced precision the model may become less reliable and produce incorrect or irrelevant results.
- Making the wrong decisions If the model is used to make decisions, these can be compromised and have negative impacts.
- Loss of confidence in the model If the model's performance deteriorates, users may lose confidence in its ability to deliver reliable results.
Model drift detection and management
It is essential to constantly monitor the performance of the models deployed and to put in place mechanisms to detect and manage model drift.
Here are some common approaches:
- Monitoring performance metrics Track changes in model performance indicators (precision, recall, F1-score, etc.) over time.
- Analysis of input data Compare the new data with the training data to identify significant changes.
- Use of drift detection techniques using statistical or machine-learning methods to automatically detect signs of drift.
- Re-training the model update the model with new data to adapt it to the changes observed.
