Baseline

Baseline for drift detection and model performance monitoring. A Baseline defines a reference point for comparing production data against expected patterns. It serves as the foundation for detecting data drift, model performance degradation, and distributional changes in ML systems.

Example

# Create a static baseline from training data
baseline = Baseline(
    name="production_baseline_v1",
    model_id=model.id,
    environment=EnvType.PRE_PRODUCTION,
    dataset_id=training_dataset.id,
    type_="STATIC"
).create()

# Create a rolling 30-day baseline
rolling_baseline = Baseline(
    name="rolling_30day",
    model_id=model.id,
    environment=EnvType.PRODUCTION,
    type_="ROLLING_WINDOW",
    window_bin_size=WindowBinSize.DAY,
    offset_delta=30
).create()

# Monitor drift detection
print(f"Baseline '{baseline.name}' has {baseline.row_count} records")
print(f"Created: {baseline.created_at}")

Baselines are immutable once created. To modify baseline parameters, create a new baseline and update your monitoring configurations.

Initialize a Baseline instance. Creates a baseline configuration for drift detection and monitoring. The baseline serves as a reference point for comparing production data against expected patterns.

Parameters

str

required

Human-readable name for the baseline. Should be descriptive and unique within the model context.

UUID | str

required

UUID of the model this baseline belongs to. Must be a valid model that exists in the Fiddler platform.

EnvType

required

Environment type (PRE_PRODUCTION or PRODUCTION). Determines the data environment this baseline monitors.

str

required

Baseline type. Supported values:

“STATIC”: Fixed dataset reference (requires dataset_id)
“ROLLING_WINDOW”: Sliding time window (requires offset_delta)
“PREVIOUS_PERIOD”: Previous time period comparison

UUID | str | None

default:"None"

UUID of the reference dataset. Required for STATIC baselines, optional for time-based baselines.

int | None

default:"None"

Start timestamp for time-based baselines (Unix timestamp). Defines the beginning of the reference period.

int | None

default:"None"

Time offset in days for rolling/previous period baselines. For ROLLING_WINDOW: size of the sliding window. For PREVIOUS_PERIOD: how far back to compare.

WindowBinSize | str | None

default:"None"

Aggregation window for time-series analysis. Controls how data is grouped for comparison.

Example

# Static baseline from training data
baseline = Baseline(
    name="training_baseline_v2",
    model_id=model.id,
    environment=EnvType.PRE_PRODUCTION,
    dataset_id=training_dataset.id,
    type_="STATIC"
)

# Rolling 7-day window baseline
rolling_baseline = Baseline(
    name="weekly_rolling",
    model_id=model.id,
    environment=EnvType.PRODUCTION,
    type_="ROLLING_WINDOW",
    offset_delta=7,
    window_bin_size=WindowBinSize.DAY
)

# Previous month comparison baseline
monthly_baseline = Baseline(
    name="month_over_month",
    model_id=model.id,
    environment=EnvType.PRODUCTION,
    type_="PREVIOUS_PERIOD",
    offset_delta=30,
    window_bin_size=WindowBinSize.DAY
)

After initialization, call create() to persist the baseline to the Fiddler platform. The baseline configuration cannot be modified after creation.

classmethod get()

Retrieve a baseline by its unique identifier. Fetches a baseline from the Fiddler platform using its UUID. This method returns the complete baseline configuration including metadata and statistics.

Parameters

UUID | str

required

The unique identifier (UUID) of the baseline to retrieve. Can be provided as a UUID object or string representation.

Returns

The baseline instance with all configuration and metadata populated from the server.

Raises

NotFound – If no baseline exists with the specified ID.
ApiError – If there’s an error communicating with the Fiddler API.

Example

# Retrieve baseline by ID
baseline = Baseline.get(id_="550e8400-e29b-41d4-a716-446655440000")
print(f"Baseline: {baseline.name}")
print(f"Type: {baseline.type}")
print(f"Environment: {baseline.environment}")
print(f"Records: {baseline.row_count}")

# Check baseline configuration
if baseline.type == "STATIC":

    print(f"Reference dataset: {baseline.dataset_id}")

elif baseline.type == "ROLLING_WINDOW":
    print(f"Window size: {baseline.offset_delta} days")
    print(f"Bin size: {baseline.window_bin_size}")

This method makes an API call to fetch the latest baseline information from the server, including any updated statistics or metadata.

classmethod from_name()

Get the baseline instance of a model from baseline name

Parameters

str

required

Baseline name

UUID | str

required

Model identifier

Returns

Baseline instance

classmethod list()

Get a list of all baselines of a model.

Returns

Iterator[Baseline]

create()

Create a new baseline.

Returns

Baseline

delete()

Delete a baseline.

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

Example

Parameters

Example

classmethod get()

Parameters

Returns

Raises

Example

classmethod from_name()

Parameters

Returns

classmethod list()

Returns

create()

Returns

delete()

​Example

​Parameters

​Example

​classmethod get()

​Parameters

​Returns

​Raises

​Example

​classmethod from_name()

​Parameters

​Returns

​classmethod list()

​Returns

​create()

​Returns

​delete()

Example

Parameters

Example

classmethod get()

Parameters

Returns

Raises

Example

classmethod from_name()

Parameters

Returns

classmethod list()

Returns

create()

Returns

delete()