Model Drift
The degradation of the ML model’s predictive power as a result of changes in the digital environment and variables like data and concepts.
What is Model Drift?
Model drift or model decay is the degradation of the ML model’s predictive ability due to alterations in the digital environment and subsequent changes in variables like data and concepts.
Model drift results in reduced accuracy of predictions derived from new input values as compared to the performance during model training. Model drift comprises of the following categories:
Concept drift: A type of model drift due to the changes in the properties of dependent variables or the target to be predicted over time.
Data drift: A shift observed due to changes in the statistical properties of the independent variables, such as feature distributions.
Upstream data changes: These are operational data changes in data pipelines like changes in measurement systems such as miles to kilometers.
For example, a model trained in 2020 to classify spam emails might underperform in 2022 as spammers also upgrade day by day.
Why is Detecting Model Drift Important?
Monitoring ML models for drift detection is necessary to confirm the predictive power of models over time. ML model and pipeline monitoring help compare the performance of production models with their initial performance attained during model training. Measuring model drift helps identify model staleness, data quality issues, adversarial inputs, and propagation of inaccurate results.
Identifying the exact drift type will help take corrective measures such as retraining the model, training with weighted data, online learning, feature dropping or replacing the model with a new one, etc. These methods ensure that the model performs at an acceptable level without adding any negative business value.
Staying Ahead of Model Drift
Detecting model drift lacks a definitive approach and makes model drift a big challenge. Also, detecting and addressing model drift in thousands of production models poses a more significant challenge. This makes using proactive solutions a necessity.
Model monitoring tools like the Censius AI Observability Platform monitor ML pipelines, identify model drifts, and take corrective actions such that model performance is acceptable. The data drift and concept drift monitors of the Censius AI Observability Platform help detect drifts before business gets impacted and ensure that your ML models are running smoothly in production to attain desired business goals.
Further Reading
How to Detect and Overcome Model Drift in MLOps
Model drift and ensuring a healthy machine learning lifecycle
Model Drift in Machine Learning