What is model drift?
Model drift occurs when the underlying data changes, causing AsscherAi's answers to degrade, as seen when a production schedule dashboard's numbers no longer match actual production, requiring the operations manager to retrain the model using new data to maintain accuracy.
The operations manager at a large manufacturing plant checks the production schedule dashboard every morning, only to find that the answers provided by AsscherAi, the natural language query platform, are no longer aligned with the actual production numbers. This discrepancy is an example of model drift, which occurs when the underlying data changes, causing the answers provided by a machine learning model to degrade over time.
Model drift refers to the phenomenon where a machine learning model's performance deteriorates as the data it was trained on becomes less representative of the current data. This can happen when the underlying data distribution changes, such as when a new product is introduced or a new supplier is added.
Definition and distinction
Model drift is often confused with concept drift, but they are not the same thing. Concept drift refers to a change in the underlying concept or relationship between the data, whereas model drift refers to the degradation of the model's performance due to changes in the data distribution. To illustrate the difference, consider a model that predicts customer churn based on demographic data. If the demographics of the customer base change over time, the model's performance may degrade due to model drift. However, if the underlying concept of customer churn changes, such as when a new competitor enters the market, the model may need to be retrained to account for the new concept.
A shift supervisor at a large retail chain may notice that the model's predictions for customer churn are no longer accurate, and upon investigation, discovers that the demographics of the customer base have changed significantly. In this case, the model is experiencing model drift, and the supervisor may need to retrain the model using the new data.
Applicability in a large organisation
Model drift can occur in any large organisation that uses machine learning models to make predictions or decisions. For example, a category manager at a large retailer may use a model to predict sales of different products, but if the sales data changes over time, the model's performance may degrade. Similarly, a maintenance manager at a large manufacturing plant may use a model to predict equipment failures, but if the equipment or maintenance schedules change, the model's performance may degrade.
In a large organisation, model drift can be mitigated by regularly monitoring the performance of the model and retraining it as necessary. This can be done by tracking key performance indicators, such as accuracy or precision, and retraining the model when these indicators fall below a certain threshold. Additionally, organisations can use techniques such as data augmentation or transfer learning to adapt the model to changes in the data distribution.
What model drift does not do
Model drift does not imply that the underlying concept or relationship between the data has changed. It simply means that the model's performance has degraded due to changes in the data distribution. For example, a model that predicts employee turnover based on job satisfaction data may experience model drift if the demographics of the employee base change, but this does not mean that the underlying concept of employee turnover has changed.
Most people get model drift wrong by assuming that it is a rare occurrence or that it can be easily mitigated by simply retraining the model. However, model drift is a common problem in machine learning, and it requires careful monitoring and maintenance to ensure that the model remains accurate and reliable. For more information on how to mitigate model drift, you can visit our website.
When to use alternative approaches
In some cases, model drift may not be the best approach to solving a problem. For example, if the underlying concept or relationship between the data is changing rapidly, it may be better to use a different approach, such as online learning or incremental learning. These approaches can adapt to changes in the data distribution in real-time, without requiring the model to be retrained.
Alternatively, if the data is highly imbalanced or noisy, it may be better to use techniques such as data preprocessing or feature engineering to improve the quality of the data before training the model. In these cases, model drift may not be the primary concern, and alternative approaches may be more effective. If you are unsure about which approach to use, you can contact us for more information.
Frequently asked questions
What causes model drift in machine learning models
Model drift is caused by changes in the underlying data distribution, such as new products or suppliers, making the trained model less representative of current data.
How does model drift differ from concept drift
Model drift refers to degradation of model performance due to data distribution changes, whereas concept drift refers to changes in the underlying concept or relationship between the data.
Can model drift be mitigated in large organisations
Yes, model drift can be mitigated by regularly monitoring model performance, retraining as necessary, and using techniques like data augmentation or transfer learning to adapt to changes.
When should alternative approaches be used instead of mitigating model drift
Alternative approaches like online learning or incremental learning should be used when the underlying concept or relationship between the data is changing rapidly, or when data is highly imbalanced or noisy.