MLOps doesn’t end with deployment. You must monitor the model’s performance in the real world.
1. Logging Production Metrics¶
You can use MLflow to log metrics from your production inference server to track accuracy over time.
2. Drift Detection¶
If the distribution of your input data changes (Data Drift), your model’s accuracy will likely drop. Use MLflow to log histograms of input features and compare them with the training distribution.