Knowledge Graph — Coursera Notes › Academic disciplines › Information Technology / Computer Science › AI/ML Engineer › Ongoing monitoring and maintenance

Model drift

concept · part of Ongoing monitoring and maintenance

Model drift is the phenomenon where ML models become less accurate over time as the data they process evolves. AI/ML engineers are responsible for detecting drift and retraining models to maintain effectiveness. For example, a customer churn model may drift as customer behavior changes, requiring periodic retraining with new data.

Model drift can be categorized into concept drift (changes in the relationship between input and output) and data drift (changes in the input data distribution). Monitoring tools like statistical tests (e.g., Kolmogorov-Smirnov) or drift detection algorithms (e.g., ADWIN) are used to identify drift. Retraining strategies include scheduled retraining, triggered retraining upon drift detection, or online learning.

Drift detection can be automated using tools like Evidently AI, which compares data distributions and generates reports. Retraining workflows may involve automated pipelines that retrain and redeploy models when drift exceeds a threshold.

Mitigation involves implementing monitoring solutions (e.g., Azure Monitor, MLflow) to track performance metrics and retraining the model periodically to adapt to new data patterns.

• Optimize for scalability: ensure that your deployment infrastructure can handle increased load by using container orchestration solutions such as Kubernetes.

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