Model Schema Editing Guide
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Note:
The UI-based Model Editor feature is currently in
Available as of v25.4
This guide explains how to edit your model's schema in Fiddler to better align with production data. Schema editing helps you maintain accurate monitoring as your data evolves.
Key capabilities
Adjust numeric feature ranges when real-world data deviates from your original sample data
Edit categorical feature values to add or remove categories as new patterns emerge
Add metadata columns to include additional contextual information for improved insights
Access the Schema tab
Navigate to the Model Page of your desired model
Select the Schema tab
Edit numeric column range
Find the numeric column you want to adjust
Select the edit icon (✏️) next to the column name
In the dialog box, modify the minimum and/or maximum values
Select Update to save your changes
Impact of changes
Data drift metrics: Changes apply to all data, including historical data
A job will run to recalculate aggregates and update metrics
Data integrity metrics: Changes only apply to new data going forward
Access the Schema tab
Navigate to the Model Page of your desired model
Select the Schema tab
Edit categorical column
Locate the categorical column you want to modify
Select the edit icon (✏️) next to the column name
Add or remove categories as needed
Select Update to save your changes
Impact of changes
For both data drift and data integrity metrics:
Changes only apply to new data going forward
Historical data remains unchanged
Access the Schema tab
Navigate to the Model Page of your desired model
Select the Schema tab
Add a Metadata Column
Select Add Metadata
Provide the required information:
Column Name: Specify the name of the new metadata column
Data Type: Choose a data type (integer, float, string, or boolean)
Range: For numeric types, define minimum and maximum values
Select Add to save
Impact of Changes
New metadata columns are effective immediately for new data
Analyze production data to set realistic range values and identify useful metadata columns
Monitor metrics after adjustments to ensure changes effectively address your needs
Use annotations for transparency to maintain a clear history of schema changes
Can I change column names or data types?
No, changing column names or data types is not supported.
What if I make a mistake?
You can edit the values again and save the updated schema.
How long do changes take to apply?
Application time depends on dataset size and complexity. For example, processing 10 million rows over six months takes approximately 12 minutes.
Can I delete a metadata column?
No, metadata columns cannot be deleted once added.
What happens if I add a category that doesn't exist in the data?
The category will be listed but won't impact existing calculations.