Data Drift vs. Concept Drift and Why Monitoring for Them is Important

As the volume of data generated and used in organizations grows, maintaining the accuracy and relevance of data used for modeling and analysis becomes increasingly important. Machine learning (ML) models are trained on data and can become less accurate over time due to changes in the data distribution caused by various factors such as changes in the data sources, environmental factors, or user behavior. Two common challenges that can impact ML models on production are data drift and concept drift. In this blog, we'll explore the differences between these two types of drift and why monitoring for them is crucial.

Concept drift vs. data drift

The key difference between data drift and concept drift is that data drift refers to changes in the input data used for modeling, while concept drift refers to changes in the relationships between the input features and the target variable that a model is trying to predict.

In other words, data drift occurs when the input data changes, but the underlying relationships between variables remain the same. Concept drift, on the other hand, occurs when the relationships between variables change, even if the input data remains the same.

Data drift: The causes and how to detect it

Changes in the input data used to train a model or make predictions can be caused by changes in the data sources, data collection process, or in the data distribution itself over time.

Examples of data drift

• A model trained on sales data from the holiday season may not perform well when used to make predictions during other times of the year when sales patterns are different.
• A model was trained on data collected from a specific set of sensors, but over time new sensors are added, or old ones are replaced, leading to changes in the input data.
• A model was trained using data from one region or market, but as the business expands to new regions or markets, the input data changes.

Data drift can be detected in several ways, including:

• Statistical tests: Use statistical tests like the Kolmogorov-Smirnov test or the Jensen-Shannon divergence to detect changes in the distribution of your input data.
• Model performance metrics: Monitor changes in the performance metrics of your ML model, such as accuracy or F1-score, to detect if they are degrading over time.
• Visualization: Visualize the distribution of your input data and compare it to previous data to visually detect any changes.

Concept drift: The causes and how to detect it

Changes in the relationships between the input features and the target variable that a model is trying to predict means that even if the input data remains the same, the underlying relationships between the variables may change over time, which can impact the model's accuracy.

Examples of concept drift

• A model was trained to predict customer churn based on historical data, but a new marketing campaign is launched that changes how customers behave.
• A model was trained to predict the likelihood of an individual defaulting on a loan based on their credit score, but changes in the economy or industry can cause the underlying relationship between credit score and default risk to change.
• A model was trained to predict the risk of equipment failure based on sensor data, but over time the underlying relationship between sensor readings and equipment failure may change due to changes in the equipment or the environment.

Concept drift can be detected in several ways, including:

• Monitoring performance metrics: Monitor changes in the performance metrics of your ML model over time, such as accuracy or F1-score, to detect if they are degrading.
• Comparing models: Compare the predictions of your ML model over time and compare it to previous models to detect any differences.
• Monitoring prediction output confidence levels: Detect when the model is struggling to make accurate predictions and investigate the cause of the issue.

Why monitoring for drift is important

In order for businesses to make informed decisions, they need to ensure that ML models remain accurate and reliable over time. It is important to monitor for both types of drift and should be a key part of your post-deployment strategy.

Better model performance

Monitoring for drift allows you to detect and correct the drift in real-time. This ensures that the models are always up-to-date and accurate, leading to better performance. Organizations can refine their models to better capture changes in the data or underlying relationships between variables, leading to better predictions and more accurate insights.

Avoiding bias

Drift can often lead to bias in the model, where certain groups may be unfairly disadvantaged. Monitoring for drift allows you to detect and correct for bias, leading to fairer and more ethical models.

Compliance

In regulated industries such as healthcare and finance, monitoring for drift is essential to ensure compliance with regulations and standards.

Implementing a machine learning monitoring system

WhyLabs provides a comprehensive set of monitoring and alerting features that can help detect drift proactively so you can prevent model failures and improve model performance. Monitor model performance metrics over time, identify changes in input data distributions and visualize the distribution of your input data to detect any changes.

With the WhyLabs AI Observatory, identify the root cause of drift by tracking data lineage, visualizing feature importance, and identifying the most important features contributing to the drift. In addition, once drift is detected, actionable insights enable you to fix the issue. This includes recommendations for retraining your models on new data, adjusting model parameters, and refining or updating features to improve performance.

Schedule a call with us to learn how WhyLabs can help detect, identify, and fix drift in your ML models, or sign up for a free account to start monitoring your models right away.