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Documentation Index

Fetch the complete documentation index at: https://handbook.fiddler.ai/llms.txt

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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.

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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.

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Parameters

ParameterTypeRequiredDefaultDescription
namestr✗NoneHuman-readable name for the baseline. Should be descriptive and unique within the model context.
model_id`UUIDstr`✗None
environmentEnvType✗NoneEnvironment type (PRE_PRODUCTION or PRODUCTION). Determines the data environment this baseline monitors.
type_str✗NoneBaseline type. Supported values: “STATIC”: Fixed dataset reference (requires dataset_id); “ROLLING_WINDOW”: Sliding time window (requires offset_delta); “PREVIOUS_PERIOD”: Previous time period comparison
dataset_id`UUIDstrNone`✗
start_time`intNone`✗None
end_time`intNone`✗None
offset_delta`intNone`✗None
window_bin_size`WindowBinSizestrNone`✗

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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.

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classmethod get(id_)

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.

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Parameters

ParameterTypeRequiredDefaultDescription
id_`UUIDstr`✗None

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Returns

The baseline instance with all configuration and metadata populated from the server. Return type: Baseline

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Raises

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

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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.

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classmethod from_name(name, model_id)

Get the baseline instance of a model from baseline name

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Parameters

ParameterTypeRequiredDefaultDescription
namestr✗NoneBaseline name
model_id`UUIDstr`✗None

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Returns

Baseline instance Return type: Baseline

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classmethod list(model_id, type_=None, environment=None)

Get a list of all baselines of a model. Return type: Iterator[Baseline]

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create()

Create a new baseline. Return type: Baseline

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delete()

Delete a baseline. Return type: None