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Missing Values
In real world data, there are often cases where a particular data element is missing. It is important to monitor the changes in missing values in order to spot and handle cases in which the model has not been trained to deal with.
Causes of missing values include:
- Serving environment fault
- Data store / provider schema changes
- Changes in internal API
- Changes in model subject input
For this monitor, the following comparison methods are available:
Configuration may slightly vary depending on the comparison method you choose.
You may select as many fields as you want (from features/raw inputs) 😊
Note that the monitor will run on each selected field separately.
For the fields you chose in the previous step, the monitor will raise an alert if the comparison between the inspection period and the baseline leads to a conclusion outside your threshold boundaries.
This step is important to make sure you have the right amount of alerts that fits your needs. For anomaly detection method, use the monitor preview to help you decide what is the appropriate sensitivity level.
Last modified 10mo ago