ModelSchema

ModelSchema contains the specification of all columns that a model expects to receive, including their data types, constraints, and metadata. This schema is used by Fiddler for data validation, monitoring, and analysis purposes. The schema acts as a contract between your model and Fiddler, ensuring that incoming data conforms to expected formats and enabling proper drift detection, data quality monitoring, and other features.

Examples

Creating a model schema:

schema = ModelSchema(
    columns=[
        Column(name="age", data_type=DataType.INTEGER, min=0, max=120),
        Column(name="income", data_type=DataType.FLOAT, min=0),
        Column(name="category", data_type=DataType.CATEGORY,

        categories=["A", "B", "C"])
    ]
)

Accessing columns by name:

age_column = schema["age"]
print(age_column.data_type)

Adding a new column:

new_column = Column(name="score", data_type=DataType.FLOAT)
schema["score"] = new_column

Removing a column:

del schema["age"]

Attributes

Schema version

List of columns

`getitem()`

Get column by name

Returns

Column

`setitem()`

Set column by name

`delitem()`

Delete column by name

Fiddler Python Client SDK

Fiddler Evals SDK

Fiddler OTel SDK

Fiddler LangGraph SDK

Fiddler LangChain SDK

Fiddler Google ADK SDK

Fiddler Strands Agent SDK

Fiddler OTel JS SDK

Fiddler LangGraph JS SDK

Fiddler LangChain JS SDK

Examples

Attributes

`getitem()`

Returns

`setitem()`

`delitem()`

​Examples

​Attributes

​__getitem__()

​Returns

​__setitem__()

​__delitem__()

Examples

Attributes

`getitem()`

Returns

`setitem()`

`delitem()`