ModelSchema

Defines the complete schema structure for a model’s input data. 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"]

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

schema_version

Schema version

columns

List of columns

getitem()

Get column by name

Returns

Column

setitem()

Set column by name

delitem()

Delete column by name

ImageEmbedding

ModelSpec

⌘I

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

model_config

schema_version

columns

getitem()

Returns

setitem()

delitem()

​Examples

​model_config

​schema_version

​columns

​getitem()

​Returns

​setitem()

​delitem()

Examples

model_config

schema_version

columns

getitem()

Returns

setitem()

delitem()