Model drift

What is data drift?

To understand the specifics of model drift, discover three main types of drift machine learning models are subject to. 

1. Concept drift

Concept drift occurs when the relationship between input features and target labels changes over time.

Example: A credit risk model trained on past financial data may become inaccurate due to economic shifts.

Concept drift can be further classified into the following categories. 

• Sudden drift: an abrupt change in data patterns (e.g., a global pandemic altering consumer behavior).
• Gradual drift: A slow shift in data distributions over time (e.g., evolving slang in social media sentiment analysis).
• Recurring drift: Seasonal patterns that affect the model periodically (e.g., holiday sales affecting customer purchases).

2. Data drift (covariate shift)

This type of drift happens when the distribution of input features changes while the relationship with the target variable remains stable.

Example: A facial recognition model may perform poorly if it was trained on one demographic but encounters new age groups or ethnicities.

3. Label drift

When the distribution of target labels changes over time, this can be considered “label drift”. 

Example: A spam classifier may misclassify emails if the definition of spam changes due to new phishing techniques.

Causes of model drift

Model drift is an inevitable challenge all machine learning algorithms face over time and one of the main reasons why fine-tuning models is essential. 

Here are the most common causes of model drift. 

• Changing user behavior: customer preferences evolve over time.
• Market or economic shifts: economic downturns or new trends affect predictions.
• Regulatory changes: legal updates may alter data definitions.
• Sensor degradation: in IoT or medical devices, hardware wear can change input data quality.

There are several ways for machine learning engineers to detect model drift: 

• Monitoring performance metrics by tracking accuracy, precision, recall, and AUC over time.
• Statistical tests using methods like Kullback-Leibler divergence or Kolmogorov-Smirnov test to compare distributions.
• Drift detection algorithms like ADWIN (Adaptive Windowing) to detect drift by dynamically adjusting the observation window or Page-Hinkley Test to identify mean shifts in data streams.

Best practices for data drift management

Machine learning teams should regularly test the performance of machine learning algorithms to spot drift before it creates a negative impact across the business functions it was designed to improve. 

If, using the tools above, ML model drift was detected, here are the tools engineers can use to address the challenge: 

• Regular model retraining presumes updating the model with new, relevant data.
• Feature engineering adjustments: Adapting feature selection based on new trends.
• Online learning by continuously updating the model with real-time data.
• Human-in-the-loop validation via periodic audits and expert reviews to ensure accuracy.

Conclusion

Model drift is an unavoidable challenge in machine learning systems deployed in dynamic environments. Continuous monitoring, retraining, and adaptive learning strategies are essential to maintain model accuracy and reliability over time.