Updating Model Schema - Fiddler Documentation

Learn how to modify your model’s schema after initial creation by adding new columns.

Overview

Sometimes you need to add new columns to an existing model in production. Common scenarios include:

Adding new features that weren’t in the original training data
Including additional metadata for monitoring purposes
Extending the model with derived features
Adding tracking columns for business metrics

Fiddler allows you to update your model schema programmatically using the Python client’s add_column() method.

Availability: This feature requires Fiddler Python Client SDK version 3.11 or later.For detailed API reference, see Model.add_column().

Prerequisites

An existing model in Fiddler
Python client installed and initialized (version 3.11+)
Appropriate permissions to modify the model

Adding a Column

Use the add_column() method on your model instance to add a new column:

Basic Example

import fiddler as fdl
from fiddler import Column, DataType

# Fetch existing model
model = fdl.Model.from_name(
    name="fraud_detector",
    project_id="YOUR_PROJECT_ID"
)

# Define new column (bins are optional — auto-generated from min/max if omitted)
new_column = Column(
    name="transaction_amount",
    data_type=DataType.FLOAT,
    min=0.0,
    max=100000.0,
    bins=[0, 100, 500, 1000, 5000, 10000, 50000, 100000]
)

# Add to model schema
model.add_column(column=new_column, column_type='metadata')

Column Types

The column_type parameter specifies where the column will be used in your model. Available types:

'inputs': Model input features used for predictions
'outputs': Model prediction outputs (probabilities, scores, etc.)
'targets': Ground truth labels for evaluation
'metadata': Tracking/monitoring data (default)

Data Type Examples

Fiddler supports the following data types for model columns:

Integer (DataType.INTEGER): Whole numbers (e.g., age, count)
Float (DataType.FLOAT): Decimal numbers (e.g., price, score, probability)
Category (DataType.CATEGORY): Categorical values from a predefined set
String (DataType.STRING): Text data
Boolean (DataType.BOOLEAN): True/false values
Vector (DataType.VECTOR): Multi-dimensional numerical arrays (embeddings)
Timestamp (DataType.TIMESTAMP): Date and time values

Numeric Column (Integer)

from fiddler import Column, DataType

age_col = Column(
    name="customer_age",
    data_type=DataType.INTEGER,
    min=18,
    max=100
)
model.add_column(column=age_col, column_type='metadata')

Numeric Column (Float)

score_col = Column(
    name="risk_score",
    data_type=DataType.FLOAT,
    min=0.0,
    max=1.0
)
model.add_column(column=score_col, column_type='outputs')

Categorical Column

category_col = Column(
    name="product_category",
    data_type=DataType.CATEGORY,
    categories=["Electronics", "Clothing", "Food", "Books"]
)
model.add_column(column=category_col, column_type='inputs')

String Column

text_col = Column(
    name="customer_feedback",
    data_type=DataType.STRING
)
model.add_column(column=text_col, column_type='metadata')

Boolean Column

bool_col = Column(
    name="is_premium_customer",
    data_type=DataType.BOOLEAN
)
model.add_column(column=bool_col, column_type='metadata')

Vector Column (Embeddings)

embedding_col = Column(
    name="text_embedding",
    data_type=DataType.VECTOR,
    n_dimensions=768
)
model.add_column(column=embedding_col, column_type='inputs')

Timestamp Column

timestamp_col = Column(
    name="transaction_time",
    data_type=DataType.TIMESTAMP
)
model.add_column(column=timestamp_col, column_type='metadata')

Important Considerations

Historical Data

Adding a column doesn’t automatically populate historical data. The new column will have null values for all past events. Only newly published events will contain values for the added column. Additionally, the baseline dataset won’t have data for this new column. If you need to compute drift metrics for the new column, upload a new baseline dataset that includes the column data:

import pandas as pd

# Prepare baseline data with the new column included
baseline_df = pd.DataFrame({
    "feature1": [...],
    "feature2": [...],
    "region": ["US", "EU", "APAC", ...]  # New column added to schema
})

# Upload new baseline dataset
baseline_publish_job = model.publish_batch(
    source=baseline_df,
    environment=fdl.EnvType.PRE_PRODUCTION,
    dataset_name='baseline_with_new_column',
)
print(f'Baseline upload initiated with Job ID = {baseline_publish_job.id}')

Schema Validation

The column definition must pass Fiddler’s validation rules:

Column names must be unique within the model
Data types must be valid
Numeric columns should specify min/max ranges
Numeric columns may optionally specify custom bins (must span [min, max], be strictly increasing, at most 16 boundary values; integer columns require integer bin values)
Categorical columns should specify categories
Vector columns must specify dimensions

Publishing Data

After adding a column, remember to include it when publishing new events:

import fiddler as fdl
import pandas as pd

# Add new column to model
model.add_column(
    column=Column(name="region", data_type=DataType.STRING),
    column_type='metadata'
)

# Publish events including the new column
events_df = pd.DataFrame({
    "timestamp": [...],
    "feature1": [...],
    "feature2": [...],
    "region": ["US", "EU", "APAC", ...]  # New column
})

model.publish_batch(source=events_df, environment=fdl.EnvType.PRODUCTION)

Common Use Cases

Adding Multiple Columns

# Define multiple columns
columns_to_add = [
    Column(name="customer_segment", data_type=DataType.INTEGER, min=1, max=5),
    Column(name="region", data_type=DataType.STRING),
    Column(name="is_returning", data_type=DataType.BOOLEAN)
]

# Add each column
for col in columns_to_add:
    model.add_column(column=col, column_type='metadata')
    print(f"Added column: {col.name}")

Adding a Feature Column

# Add a new feature that wasn't in original training data
new_feature = Column(
    name="days_since_last_purchase",
    data_type=DataType.INTEGER,
    min=0,
    max=365
)
model.add_column(column=new_feature, column_type='inputs')

Error Handling

Duplicate Column Names

try:
    model.add_column(
        column=Column(name="existing_column", data_type=DataType.STRING),
        column_type='metadata'
    )
except ValueError as e:
    print(f"Error: {e}")
    # Output: Column 'existing_column' already exists in model schema

Complete Example

Here’s a complete workflow for adding columns to an existing model:

import fiddler as fdl
from fiddler import Column, DataType
import pandas as pd

# Initialize Fiddler client
fdl.init(
    url="https://your-instance.fiddler.ai",
    token="your-api-token"
)

# Get existing model
model = fdl.Model.from_name(
    name="credit_risk_model",
    project_id="my-project-id"
)

print(f"Current columns: {[col.name for col in model.schema.columns]}")

# Add new metadata columns
new_columns = [
    Column(
        name="customer_tier",
        data_type=DataType.CATEGORY,
        categories=["Bronze", "Silver", "Gold", "Platinum"]
    ),
    Column(
        name="account_age_days",
        data_type=DataType.INTEGER,
        min=0,
        max=10000
    ),
    Column(
        name="transaction_history_summary",
        data_type=DataType.STRING
    )
]

# Add each column
for col in new_columns:
    try:
        model.add_column(column=col, column_type='metadata')
        print(f"✓ Added: {col.name}")
    except ValueError as e:
        print(f"✗ Failed to add {col.name}: {e}")

# Verify columns were added
print(f"Updated columns: {[col.name for col in model.schema.columns]}")

# Publish new events with the added columns
new_events = pd.DataFrame({
    # Existing columns
    "timestamp": ["2024-01-01T12:00:00Z"],
    "credit_score": [720],
    "annual_income": [75000],
    
    # Newly added columns
    "customer_tier": ["Gold"],
    "account_age_days": [365],
    "transaction_history_summary": ["Regular activity, no delinquencies"]
})

job = model.publish_batch(source=new_events, environment=fdl.EnvType.PRODUCTION)
print(f"Published events with new columns. Job ID: {job.id}")

Frequently Asked Questions (FAQ)

Q: Can I modify an existing column? A: add_column() is only for adding new columns. To modify an existing column’s properties (like min, max, bins, or categories), update the property on the schema and call model.update():

model = fdl.Model.get(id_="abc-123")
model.schema["credit_score"].bins = [350, 500, 650, 850]
model.update()

Note: Bin boundaries must span the column’s existing [min, max] range. If you are also changing the range, update min and max in the same call.

See Customizing Your Model Schema for details. Q: What happens to existing alerts and monitors? A: Existing alerts and monitors continue to work. However, you may want to create new monitors for the added columns. Q: Can I add multiple columns at once? A: You need to call add_column() separately for each column. The method updates the model after each addition.

​Overview

​Prerequisites

​Adding a Column

​Basic Example

​Column Types

​Data Type Examples

​Numeric Column (Integer)

​Numeric Column (Float)

​Categorical Column

​String Column

​Boolean Column

​Vector Column (Embeddings)

​Timestamp Column

​Important Considerations

​Historical Data

​Schema Validation

​Publishing Data

​Common Use Cases

​Adding Multiple Columns

​Adding a Feature Column

​Error Handling

​Duplicate Column Names

​Complete Example

​Related Documentation

​Frequently Asked Questions (FAQ)

Overview

Prerequisites

Adding a Column

Basic Example

Column Types

Data Type Examples

Numeric Column (Integer)

Numeric Column (Float)

Categorical Column

String Column

Boolean Column

Vector Column (Embeddings)

Timestamp Column

Important Considerations

Historical Data

Schema Validation

Publishing Data

Common Use Cases

Adding Multiple Columns

Adding a Feature Column

Error Handling

Duplicate Column Names

Complete Example

Related Documentation

Frequently Asked Questions (FAQ)