Model Schema Editing Guide

🚧 Note:

• The UI-based Model Editor feature is currently in public preview

• Available as of v25.4

Overview

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

Adjusting numeric feature ranges

Access the Schema tab

1. Navigate to the Model Page of your desired model

2. Select the Schema tab

Edit numeric column range

1. Find the numeric column you want to adjust

2. Select the edit icon (✏️) next to the column name

3. In the dialog box, modify the minimum and/or maximum values

4. 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

Editing categorical variables

Access the Schema tab

1. Navigate to the Model Page of your desired model

2. Select the Schema tab

Edit categorical column

1. Locate the categorical column you want to modify

2. Select the edit icon (✏️) next to the column name

3. Add or remove categories as needed

4. 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

Adding metadata columns

Access the Schema tab

1. Navigate to the Model Page of your desired model

2. Select the Schema tab

Add a Metadata Column

1. Select Add Metadata

2. 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

3. Select Add to save

Impact of Changes

• New metadata columns are effective immediately for new data

Best Practices

• 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

Frequently Asked Questions

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.