<ul><li>Concept drift, feature drift, and label drift are the main types of data drift that can occur in machine learning models.</li><li>Implementing model monitoring techniques, such as tracking various metrics and calculating distribution changes, can help detect data drift.</li><li>Retraining the model using updated data is an effective approach to handle data drift.</li><li>It is important to regularly monitor and address data drift in machine learning projects to ensure model accuracy and reliability.</li></ul>

Data Drift and Model Monitoring in Production: Why It Matters and How to Handle It

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