We do not directly detect anomalies but we have two types of checks to run when there are no actuals or ground truth labels: 1) data drift to compare the statistical distribution of the input features to the past 2) prediction drift to compare the distribution of the model predictions to the past

To control the sensitivity of monitoring you can manually decide if you want to monitor all features, or maybe only the most important ones. This is not automated yet.

We also generate a few dashboards to show the relationship between the features and predicted values - to help with visual debugging.

We plan to add some unsupervised approaches like outlier detection later on. But for the moment we do not have checks on the level of individual objects in the data.