In my opinion, the most effective way to detect and handle concept drift in real-world MLOps systems is to combine continuous monitoring with automated retraining strategies, rather than relying on one-time checks. Monitoring should include tracking model performance metrics like accuracy, precision, or error rates over time, along with statistical tests to compare current data distributions with the training data to spot any significant changes early. Techniques like data drift detection, concept drift detection algorithms, and even simple threshold-based alerts can help signal when the model is no longer performing as expected. To handle drift, organizations should implement pipelines that allow for periodic or triggered retraining using fresh data, along with proper validation before redeployment to avoid introducing new issues. It’s also important to maintain versioning, experiment tracking, and rollback mechanisms so teams can quickly respond if a retrained model underperforms. Overall, a proactive and automated approach, combined with human oversight, ensures models stay reliable and continue delivering value as data evolves.