You can detect data drift without labels by monitoring input distributions.

Track statistical properties of each feature and compare them to training baselines. Significant changes in distributions, category frequencies, or missing rates are often early indicators of performance degradation.

Use metrics like population stability index (PSI), KL divergence, or simple threshold-based alerts for numerical features. For categorical features, monitor new or disappearing categories.

This won’t tell you exact accuracy, but it provides a strong signal that retraining or investigation is needed.The key takeaway is that unlabeled drift detection is still actionable and essential in production ML