In my opinion, the most reliable way to detect drift in MLOps pipelines is to combine performance monitoring with data-level analysis, rather than depending on a single method, because drift can show up in different ways. Tracking key metrics like accuracy, precision, or error rates over time is a good starting point, as sudden drops often signal an issue, but it’s equally important to monitor data distributions using statistical techniques to compare live data with the original training data. Setting up automated alerts based on thresholds helps teams catch drift early, and using dashboards makes trends easier to understand. Once drift is identified, the response should be structured and timely, such as retraining the model with updated data, validating it thoroughly before redeployment, and maintaining version control to allow safe rollbacks if needed. In some cases, updating features or even redesigning the model may be necessary if the underlying patterns have changed significantly. Overall, a combination of continuous monitoring, automation, and human oversight ensures that models remain accurate and reliable in changing real-world conditions.