Model - Fiddler Documentation

Represents a machine learning model in the Fiddler platform. The Model class is the central entity for ML model monitoring and management. It encapsulates the model’s schema, specification, and metadata, and provides methods for data publishing, artifact management, and monitoring operations. Key Concepts:

Schema (ModelSchema): Defines the structure and data types of model inputs/outputs
Spec (ModelSpec): Defines how columns are used (features, targets, predictions, etc.)
Task (ModelTask): The ML task type (classification, regression, ranking, etc.)
Artifacts (ModelArtifact): Deployable model code and dependencies
Surrogates (Surrogate): Simplified models for fast explanations

Lifecycle:

Create model with schema/spec (from data or manual definition)
Upload model artifacts for serving (optional)
Publish baseline/training data for drift detection
Publish production data for monitoring
Set up alerts and monitoring rules

Common Use Cases:

Tabular Models: Traditional ML models with structured data
Text Models: NLP models with text inputs and embeddings
Mixed Models: Models combining tabular and unstructured data
Ranking Models: Recommendation and search ranking systems
LLM Models: Large language model monitoring

Parameters

name

str

required

Model name, must be unique within the project version. Should be descriptive and follow naming conventions.

project_id

UUID | str

required

UUID or string identifier of the parent project.

schema

ModelSchema

required

ModelSchema defining column structure and data types. Can be created manually or generated from data.

spec

ModelSpec

required

ModelSpec defining how columns are used (inputs, outputs, targets). Specifies the model’s interface and column roles.

version

str | None

default:"None"

Optional version identifier for model versioning and A/B testing. Falls back to ‘v1’ when not specified.

input_type

str

default:"ModelInputType.TABULAR"

ModelInputType - Type of input data the model processes.

TABULAR: Structured/tabular data (default)
TEXT: Natural language text data
MIXED: Combination of structured and unstructured data

task

str

default:"ModelTask.NOT_SET"

ModelTask - Machine learning task type.

BINARY_CLASSIFICATION: Binary classification (0/1, True/False)
MULTICLASS_CLASSIFICATION: Multi-class classification
REGRESSION: Continuous value prediction
RANKING: Ranking/recommendation tasks
LLM: Large language model tasks
NOT_SET: Task not specified (default)

task_params

ModelTaskParams | None

default:"None"

ModelTaskParams - Task-specific parameters like classification thresholds, class weights, or ranking parameters.

description

str | None

default:"None"

Human-readable description of the model’s purpose, training data, or other relevant information.

event_id_col

str | None

default:"None"

Column name containing unique identifiers for each prediction event. Used for event tracking and updates.

event_ts_col

str | None

default:"None"

Column name containing event timestamps. Used for time-based analysis and drift detection.

event_ts_format

str | None

default:"None"

Format string for parsing timestamps in event_ts_col. Examples: ‘%Y-%m-%d %H:%M:%S’, ‘%Y-%m-%dT%H:%M:%SZ’

xai_params

XaiParams | None

default:"None"

XaiParams - Configuration for explainability features like explanation methods and custom feature definitions.

The model exists only locally until .create() is called. Use Model.from_data() for automatic schema/spec generation from DataFrames or files.

Examples

# Create model from DataFrame with automatic schema detection
import fiddler as fdl
import pandas as pd

df = pd.DataFrame({
    'age': [25, 35, 45],
    'income': [50000, 75000, 100000],
    'approved': [0, 1, 1]  # target
})

model = fdl.Model.from_data(
    source=df,
    name="credit_approval",
    project_id='<YOUR_PROJECT_ID>',
    task=fdl.ModelTask.BINARY_CLASSIFICATION,
    description="Credit approval model v1.0"
)
model.create()

# Publish production events
events = [
    {'age': 30, 'income': 60000, 'prediction': 0.8},
    {'age': 40, 'income': 80000, 'prediction': 0.9}
]
event_ids = model.publish(source=events)

# Get model info
print(f"Model: {model.name} (Task: {model.task})")
print(f"Columns: {len(model.schema.columns)}")
print(f"Features: {model.spec.inputs}")

download_data()

Download production or pre-production event data to a local Parquet or CSV file. Replaces the removed get_slice() and download_slice() methods.

Parameters

output_dir

Path | str

required

Directory where the output file is written. Created if it does not exist.

env_type

EnvType

required

Environment to query. One of EnvType.PRODUCTION or EnvType.PRE_PRODUCTION.

env_id

UUID | None

default:"None"

When env_type=EnvType.PRE_PRODUCTION, the UUID of the dataset to query. Ignored for PRODUCTION.

start_time

datetime | None

default:"None"

Start of the query window. PRODUCTION only. Naive datetimes are interpreted as UTC.

segment_id

UUID | None

default:"None"

UUID of a saved Segment associated with the model. Mutually exclusive with segment_definition.

segment_definition

str | None

default:"None"

Inline FQL (Fiddler Query Language) segment expression. The segment is applied transiently and not saved. Mutually exclusive with segment_id.

max_rows

int | None

default:"None"

Maximum number of rows to return. When omitted, the server applies a limit of 1,000 rows. Maximum allowed value is 10,000,000.

columns

list[str] | None

default:"None"

Column names to include. When omitted, all model columns are returned.

chunk_size

int | None

default:"1000"

Row count per download chunk. You can increase this for faster downloads when querying fewer than 1000 columns and no vector columns.

fetch_vectors

bool | None

default:"None"

When True, vector columns are included in the output. When omitted or False, vector columns are filtered out.

output_format

DownloadFormat

default:"DownloadFormat.PARQUET"

Output file format. One of DownloadFormat.PARQUET or DownloadFormat.CSV.

Returns

None. Writes a file named output.parquet (default) or output.csv (when output_format=DownloadFormat.CSV) into output_dir.

Raises

ApiError – If the request fails, the model is a draft model, or no data matches the query.

Example

from datetime import datetime, timedelta, timezone

import fiddler as fdl
import pandas as pd

model = fdl.Model.from_name(name='my_model', project_id='<YOUR_PROJECT_ID>')

end_time = datetime.now(timezone.utc)
start_time = end_time - timedelta(days=7)

model.download_data(
    output_dir='./exports',
    env_type=fdl.EnvType.PRODUCTION,
    start_time=start_time,
    end_time=end_time,
    output_format=fdl.DownloadFormat.CSV,
)

df = pd.read_csv('./exports/output.csv')

This method is not available for draft models. Caller must have READ permission on the model’s project.

classmethod get()

Retrieve a model by its unique identifier. Fetches a model from the Fiddler platform using its UUID. This is the most direct way to retrieve a model when you know its ID.

Parameters

id_

UUID | str

required

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

Returns

The model instance with all its configuration and metadata.

Raises

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

Example

# Get model by UUID
model = Model.get(id_="550e8400-e29b-41d4-a716-446655440000")
print(f"Retrieved model: {model.name} (Task: {model.task})")

# Access model properties
print(f"Project ID: {model.project_id}")
print(f"Input columns: {model.spec.inputs}")
print(f"Created: {model.created_at}")

This method makes an API call to fetch the latest model state from the server. The returned model instance reflects the current state in Fiddler.

classmethod from_name()

Retrieve a model by name within a project. Finds and returns a model using its name and project context. This is useful when you know the model name but not its UUID. Supports version-specific retrieval and latest version lookup.

Parameters

name

str

required

The name of the model to retrieve. Model names are unique within a project but may have multiple versions.

project_id

UUID | str

required

UUID or string identifier of the project containing the model.

version

str | None

default:"None"

Specific version name to retrieve. If None, behavior depends on the ‘latest’ parameter.

latest

bool

default:"False"

If True and version is None, retrieves the most recently created version. If False, retrieves the first (oldest) version. Ignored if version is specified.

Returns

The model instance matching the specified criteria.

Raises

NotFound – If no model exists with the specified name/version in the project.
ApiError – If there’s an error communicating with the Fiddler API.

Example

# Get specific version
model = Model.from_name(
    name="fraud_detector",
    project_id=project.id,
    version="v2.1"
)

# Get latest version
latest_model = Model.from_name(
    name="fraud_detector",
    project_id=project.id,
    latest=True
)

# Get first version (default behavior)
first_model = Model.from_name(
    name="fraud_detector",
    project_id=project.id
)

print(f"Model versions: {first_model.version} -> {latest_model.version}")

When version is None and latest=False, returns the first version created. This provides consistent behavior for accessing the “original” model version.

create()

Create the model on the Fiddler platform. Persists this model instance to the Fiddler platform, making it available for monitoring, data publishing, and other operations. The model must have a valid schema, spec, and be associated with an existing project.

Returns

This model instance, updated with server-assigned fields like ID, creation timestamp, and other metadata.

Raises

Conflict – If a model with the same name and version already exists in the project.
ValidationError – If the model configuration is invalid (e.g., invalid schema, spec, or task parameters).
ApiError – If there’s an error communicating with the Fiddler API.

Example

# Create model from DataFrame
model = Model.from_data(
    source=training_df,
    name="churn_predictor",
    project_id=project.id,
    task=ModelTask.BINARY_CLASSIFICATION
)

# Create on platform
created_model = model.create()
print(f"Created model with ID: {created_model.id}")
print(f"Created at: {created_model.created_at}")

# Model is now available for monitoring
assert created_model.id is not None

# Can now publish data, set up alerts, etc.
job = created_model.publish(source=production_data)

After successful creation, the model instance is updated in-place with server-assigned metadata. The same instance can be used for subsequent operations without needing to fetch it again.

update()

Update an existing model.

add_column()

Add a new column to the model schema. Updates both the schema and spec to include the new column. This allows you to extend your model with additional columns after initial creation. New in version 3.11.0

Parameters

column

Column

required

Column object defining the new column’s properties (name, data_type, etc.)

column_type

str

default:"'metadata'"

Type of column in spec. One of: ‘inputs’, ‘outputs’, ‘targets’, ‘decisions’, ‘metadata’. Defaults to ‘metadata’.

Raises

ValueError – If column already exists or column_type is invalid
BadRequest – If column definition is invalid per backend validation

Example

# Add a numeric metadata column
new_col = Column(
    name="customer_segment",
    data_type=DataType.INTEGER,
    min=1,
    max=5
)
model.add_column(column=new_col, column_type='metadata')

# Add a categorical feature
category_col = Column(
    name="region",
    data_type=DataType.CATEGORY,
    categories=["US", "EU", "APAC"]
)
model.add_column(column=category_col, column_type='inputs')

Adding a column doesn’t populate historical data; new column will be null for past events
Column names must be unique within the model
After adding a column, include it in future event publishing

classmethod list()

List models in a project with optional filtering. Retrieves all models or model versions within a project. Returns lightweight ModelCompact objects that can be used to fetch full Model instances when needed.

Parameters

project_id

UUID | str

required

UUID or string identifier of the project to search within.

name

str | None

default:"None"

Optional model name filter. If provided, returns all versions of the specified model. If None, returns all models in the project.

Yields

ModelCompact – Lightweight model objects containing id, name, and version. Call .fetch() on any ModelCompact to get the full Model instance.

Returns

Iterator[ModelCompact]

Example

# List all models in project
for model_compact in Model.list(project_id=project.id):

    print(f"Model: {model_compact.name} v{model_compact.version}")
    print(f"  ID: {model_compact.id}")

    # List all versions of a specific model
    for version in Model.list(project_id=project.id, name="fraud_detector"):

        print(f"Version: {version.version}")

        ...
        # Get full model details if needed
        full_model = version.fetch()
        print(f"  Task: {full_model.task}")
        print(f"  Created: {full_model.created_at}")

        # Convert to list for counting
        models = list(Model.list(project_id=project.id))
        print(f"Total models in project: {len(models)}")

This method returns an iterator for memory efficiency when dealing with many models. The ModelCompact objects are lightweight and don’t include full schema/spec information - use .fetch() when you need complete details.

duplicate()

Duplicate the model instance with the given version name. This call will not save the model on server. After making changes to the model instance call .create() to add the model version to Fiddler Platform.

Parameters

version

str | None

default:"None"

Version name for the new instance

Returns

Model instance

property datasets

Get all datasets associated with this model. Returns an iterator over all datasets that have been published to this model, including both production data and pre-production datasets used for baselines and drift comparison.

Yields

Dataset – Dataset objects containing metadata and data access methods. Each dataset represents a collection of events published to the model.

Example

# List all datasets for the model
for dataset in model.datasets:

    print(f"Dataset: {dataset.name}")
    print(f"  Environment: {dataset.environment}")
    print(f"  Size: {dataset.size} events")
    print(f"  Created: {dataset.created_at}")

    # Find specific dataset
    baseline_datasets = [
        ds for ds in model.datasets
        if ds.environment == EnvType.PRE_PRODUCTION
    ]
    print(f"Found {len(baseline_datasets)} baseline datasets")

This includes both production event data and named pre-production datasets. Use the Dataset objects to download data, analyze distributions, or set up baseline comparisons for drift detection.

property baselines

Get all baselines configured for this model. Returns an iterator over all baseline configurations used for drift detection and performance monitoring. Baselines define reference distributions and metrics for comparison with production data.

Yields

Baseline – Baseline objects containing configuration and reference data. Each baseline defines how drift and performance should be measured against historical or reference datasets.

Example

# List all baselines
for baseline in model.baselines:

    print(f"Baseline: {baseline.name}")
    print(f"  Type: {baseline.type}")  # STATIC or ROLLING
    print(f"  Dataset: {baseline.dataset_name}")
    print(f"  Created: {baseline.created_at}")

    # Find production baseline
    prod_baselines = [
        bl for bl in model.baselines
        if "production" in bl.name.lower()
    ]

    # Use baseline for drift comparison
    if prod_baselines:

        baseline = prod_baselines[0]
        drift_metrics = baseline.compute_drift(recent_data)

Baselines are essential for drift detection and alerting. They define the “normal” behavior against which production data is compared. Static baselines use fixed reference data, while rolling baselines update automatically with recent data.

property deployment

Fetch model deployment instance of this model.

Returns

The deployment configuration for this model.

classmethod from_data()

Create a Model instance with automatic schema generation from data. This is the most convenient way to create models when you have training data or representative samples. The method automatically analyzes the data to generate appropriate schema (column types) and spec (column roles) definitions.

Parameters

source

DataFrame | Path | str

required

Data source for schema generation. Can be:

pandas.DataFrame: Direct data analysis
Path/str: File path (.csv, .parquet, .json supported) The data should be representative of your model’s inputs/outputs.

name

str

required

Model name, must be unique within the project version. Use descriptive names like “fraud_detector_v1” or “churn_model”.

project_id

UUID | str

required

UUID or string identifier of the parent project.

spec

ModelSpec | None

default:"None"

Optional ModelSpec defining column roles. If None, automatic detection attempts to identify inputs, outputs, and targets based on column names and data patterns.

version

str | None

default:"\"v1\""

Model version identifier. If None, defaults to “v1”. Use semantic versioning like “v1.0”, “v2.1”, etc.

input_type

str

default:"ModelInputType.TABULAR"

ModelInputType - Type of input data the model processes.

TABULAR: Structured/tabular data (default)
TEXT: Natural language text data
MIXED: Combination of structured and unstructured data

task

str

default:"ModelTask.NOT_SET"

ModelTask - Machine learning task type:

BINARY_CLASSIFICATION: Binary classification (0/1, True/False)
MULTICLASS_CLASSIFICATION: Multi-class classification
REGRESSION: Continuous value prediction
RANKING: Ranking/recommendation tasks
LLM: Large language model tasks
NOT_SET: Task not specified (default)

task_params

ModelTaskParams | None

default:"None"

ModelTaskParams - Task-specific parameters:

binary_classification_threshold: Decision threshold (0.5)
target_class_order: Class label ordering
group_by: Column for ranking model grouping
top_k: Top-k evaluation for ranking

description

str | None

default:"None"

Human-readable description of the model’s purpose, training approach, or other relevant information.

event_id_col

str | None

default:"None"

Column name for unique event identifiers. Used for tracking individual predictions and enabling updates.

event_ts_col

str | None

default:"None"

Column name for event timestamps. Used for time-based analysis, drift detection, and temporal monitoring.

event_ts_format

str | None

default:"None"

Timestamp format string for parsing event_ts_col. Examples: ‘%Y-%m-%d %H:%M:%S’, ‘%Y-%m-%dT%H:%M:%SZ’

xai_params

XaiParams | None

default:"None"

XaiParams - Explainability configuration including explanation methods and custom feature definitions.

max_cardinality

int | None

default:"None"

Maximum unique values to consider a column categorical. Columns with more unique values are treated as continuous. Default is typically 100-1000 depending on data size.

sample_size

int | None

default:"None"

Number of rows to sample for schema generation. Useful for large datasets to speed up analysis. If None, uses entire dataset (up to reasonable limits).

Returns

A new Model instance with automatically generated schema and spec. The model is not yet created on the platform - call .create() to persist.

Raises

ValueError – If the data source is invalid or cannot be processed.
FileNotFoundError – If source is a file path that doesn’t exist.
ValidationError – If the generated schema/spec is invalid.

Example

import pandas as pd

# Create from DataFrame
df = pd.DataFrame({
    'age': [25, 35, 45, 55],
    'income': [30000, 50000, 70000, 90000],
    'credit_score': [650, 700, 750, 800],
    'approved': [0, 1, 1, 1],  # target
    'prediction': [0.2, 0.8, 0.9, 0.95],  # model output
    'prediction_score': [0.2, 0.8, 0.9, 0.95]  # alternative output
})

model = Model.from_data(
    source=df,
    name="credit_approval_v1",
    project_id=project.id,
    task=ModelTask.BINARY_CLASSIFICATION,
    description="Credit approval model trained on 2024 data",
    event_id_col="application_id",  # if present in real data
    event_ts_col="timestamp"        # if present in real data
)

# Review generated schema
print(f"Columns detected: {len(model.schema.columns)}")
for col in model.schema.columns:

    print(f"  {col.name}: {col.data_type}")

    # Review generated spec
    print(f"Inputs: {model.spec.inputs}")
    print(f"Outputs: {model.spec.outputs}")
    print(f"Targets: {model.spec.targets}")

    # Create on platform
    model.create()

    # Create from file
    model = Model.from_data(
        source="training_data.csv",
        name="file_based_model",
        project_id=project.id,
        task=ModelTask.REGRESSION,
        sample_size=10000  # Sample large files
    )

The automatic schema generation uses heuristics to detect column types and roles. Review the generated schema and spec before calling .create() to ensure they match your model’s actual interface. You can modify the schema and spec after creation if needed.

delete()

Delete a model and it’s associated resources.

Returns

model deletion job instance

remove_column()

Remove a column from the model schema and spec This method is only to modify model object before creating and will not save the model on Fiddler Platform. After making changes to the model instance, call .create() to add the model to Fiddler Platform.

Parameters

column_name

str

required

Column name to be removed

missing_ok

bool

default:"True"

If True, do not raise an error if the column is not found

Returns

None

Raises

KeyError – If the column name is not found and missing_ok is False

publish()

Publish data to the model for monitoring and analysis.

Deprecated: Use publish_stream() for streaming events (list of dicts) or publish_batch() for batch publishing (file path or DataFrame).

Uploads prediction events, training data, or reference datasets to Fiddler for monitoring, drift detection, and performance analysis. This is how you send your model’s real-world data to the platform.

Parameters

source

list[dict[str, Any]] | str | Path | DataFrame

required

Data to publish. Supported formats:

File path (str/Path): CSV or Parquet files.
Best for large datasets and batch uploads.
DataFrame: Pandas DataFrame with prediction events. Good for programmatic uploads and real-time data.
List of dicts: Individual events as dictionaries. Perfect for streaming/real-time publishing (max 1000 events). Each dict should match the model’s schema.

environment

EnvType

default:"EnvType.PRODUCTION"

EnvType - Data environment type:

PRODUCTION: Live production prediction data.
Used for real-time monitoring and alerting.
PRE_PRODUCTION: Training, validation, or baseline data. Used for drift comparison and model evaluation.

dataset_name

str | None

default:"None"

Name for the dataset when using PRE_PRODUCTION environment. Creates a named dataset for baseline comparisons. Not used for PRODUCTION data.

update

bool

default:"False"

Whether these events update previously published data. Set to True when republishing corrected predictions or adding ground truth labels to existing events.

Returns

Event IDs when source is list of dicts or DataFrame. Use these IDs to reference specific events later.

Job: Async job object when source is a file path. Use job.wait() to wait for completion or check job.status.

Raises

ValidationError – If the data doesn’t match the model’s schema or contains invalid values.
ApiError – If there’s an error uploading the data to Fiddler.
ValueError – If the source format is unsupported or parameters are incompatible (e.g., dataset_name with PRODUCTION).

Example

# Publish production events from DataFrame
# (assumes model is a fdl.Model instance; see Model.from_name() or Model.from_data())
import fiddler as fdl
import pandas as pd

prod_df = pd.DataFrame({
    'age': [30, 25, 45],
    'income': [60000, 45000, 80000],
    'prediction': [0.8, 0.3, 0.9],
    'timestamp': ['2024-01-01 10:00:00', '2024-01-01 11:00:00', '2024-01-01 12:00:00']
})

# Returns list of event UUIDs
event_ids = model.publish(
    source=prod_df,
    environment=fdl.EnvType.PRODUCTION
)
print(f"Published {len(event_ids)} events")

# Publish baseline data for drift comparison
job = model.publish(
    source="training_data.csv",  # File path
    environment=fdl.EnvType.PRE_PRODUCTION,
    dataset_name="training_baseline_2024"
)
job.wait()  # Wait for upload to complete
print(f"Baseline upload status: {job.status}")

# Publish real-time streaming events
events = [
    {
        'age': 35,
        'income': 70000,
        'prediction': 0.75,
        'event_id': 'pred_001',
        'timestamp': '2024-01-01 13:00:00'
    },
    {
        'age': 28,
        'income': 55000,
        'prediction': 0.45,
        'event_id': 'pred_002',
        'timestamp': '2024-01-01 13:01:00'
    }
]
event_ids = model.publish(source=events)
print(f"Published {len(events)} streaming events")

# Update existing events with ground truth
corrected_events = [
    {
        'event_id': 'pred_001',
        'ground_truth': 1,  # Actual outcome
        'timestamp': '2024-01-01 13:00:00'
    }
]
model.publish(source=corrected_events, update=True)

Schema Validation: All published data must match the model’s schema. Column names, types, and value ranges are validated.
Event IDs: Include event_id_col if specified in model config for event tracking and updates.
Timestamps: Include event_ts_col for time-based analysis and drift detection.
Batch Limits: List of dicts is limited to 1000 events per call. Use files or multiple calls for larger datasets.

Production data publishing enables real-time monitoring, alerting, and drift detection. Pre-production data creates reference datasets for comparison and model evaluation.

publish_stream()

Publish events via streaming for real-time monitoring. Sends events synchronously to Fiddler. Events are written to an in-memory queue and asynchronously persisted.

Parameters

events

list[dict[str, Any]]

required

List of event dicts matching the model’s schema. Each dict represents a single prediction event with feature values, predictions, and optional metadata (event_id, timestamp).

environment

EnvType

default:"EnvType.PRODUCTION"

EnvType - Data environment type:

PRODUCTION: Live production prediction data.
PRE_PRODUCTION: Training, validation, or baseline data.

dataset_name

str | None

default:"None"

Name for the dataset when using PRE_PRODUCTION environment. Not used for PRODUCTION data.

update

bool

default:"False"

Whether these events update previously published data. Set to True when adding ground truth labels to existing events.

Returns

Event IDs for the published events. Use these IDs to reference specific events later.

Raises

ValidationError – If the data doesn’t match the model’s schema.
ApiError – If there’s an error publishing the events to Fiddler.

Example

events = [
    {
        'age': 35,
        'income': 70000,
        'prediction': 0.75,
        'event_id': 'pred_001',
        'timestamp': '2024-01-01 13:00:00'
    },
    {
        'age': 28,
        'income': 55000,
        'prediction': 0.45,
        'event_id': 'pred_002',
        'timestamp': '2024-01-01 13:01:00'
    }
]
event_ids = model.publish_stream(events=events)
print(f"Published {len(event_ids)} streaming events")

# Update existing events with ground truth
ground_truth = [
    {'event_id': 'pred_001', 'ground_truth': 1}
]
model.publish_stream(events=ground_truth, update=True)

For large datasets, consider using publish_batch() with a file path or DataFrame instead. Streaming is best suited for real-time or near-real-time event publishing.

publish_batch()

Publish data via batch upload for monitoring and analysis. Uploads data as a file and kicks off an asynchronous server-side job. Use the returned Job object to track progress. Best suited for large datasets, historical data uploads, and baseline/reference datasets.

Parameters

source

str | Path | DataFrame

required

Data to publish. Supported formats:

File path (str/Path): CSV or Parquet files.
Best for large datasets already on disk.
DataFrame: Pandas DataFrame with prediction events. Automatically converted to Parquet (falls back to CSV).

environment

EnvType

default:"EnvType.PRODUCTION"

EnvType - Data environment type:

PRODUCTION: Live production prediction data.
PRE_PRODUCTION: Training, validation, or baseline data.

dataset_name

str | None

default:"None"

Name for the dataset when using PRE_PRODUCTION environment. Creates a named dataset for baseline comparisons. Not used for PRODUCTION data.

update

bool

default:"False"

Whether these events update previously published data. Set to True when republishing corrected predictions or adding ground truth labels to existing events.

Returns

Async job object. Use job.wait() to block until completion, or check job.status to poll.

Raises

ValidationError – If the data doesn’t match the model’s schema.
ApiError – If there’s an error uploading the data to Fiddler.
ValueError – If the source format is unsupported.

Example

# Batch upload from file
job = model.publish_batch(
    source="training_data.csv",
    environment=EnvType.PRE_PRODUCTION,
    dataset_name="training_baseline_2024"
)
job.wait()
print(f"Upload status: {job.status}")

# Batch upload from DataFrame
import pandas as pd
prod_df = pd.DataFrame({
    'age': [30, 25, 45],
    'income': [60000, 45000, 80000],
    'prediction': [0.8, 0.3, 0.9],
})
job = model.publish_batch(source=prod_df)
job.wait()

For real-time or near-real-time event publishing, consider using publish_stream() instead.

add_artifact()

Upload and deploy model artifact.

Parameters

model_dir

str | Path

required

Path to model artifact tar file

deployment_params

DeploymentParams | None

default:"None"

Model deployment parameters

Returns

Async job instance

update_artifact()

Update existing model artifact.

Parameters

model_dir

str | Path

required

Path to model artifact tar file

deployment_params

DeploymentParams | None

default:"None"

Model deployment parameters

Returns

Async job instance

download_artifact()

Download existing model artifact.

Parameters

output_dir

str | Path

required

Path to download model artifact tar file

add_surrogate()

Add a new surrogate model

Parameters

dataset_id

UUID | str

required

Dataset to be used for generating surrogate model

deployment_params

DeploymentParams | None

default:"None"

Model deployment parameters

Returns

Async job

update_surrogate()

Update an existing surrogate model

Parameters

dataset_id

UUID | str

required

Dataset to be used for generating surrogate model

deployment_params

DeploymentParams | None

default:"None"

Model deployment parameters

Returns

Async job

​Parameters

​Examples

​download_data()

​Parameters

​Returns

​Raises

​Example

​classmethod get()

​Parameters

​Returns

​Raises

​Example

​classmethod from_name()

​Parameters

​Returns

​Raises

​Example

​create()

​Returns

​Raises

​Example

​update()

​add_column()

​Parameters

​Raises

​Example

​classmethod list()

​Parameters

​Yields

​Returns

​Example

​duplicate()

​Parameters

​Returns

​property datasets

​Yields

​Example

​property baselines

​Yields

​Example

​property deployment

​Returns

​classmethod from_data()

​Parameters

​Returns

​Raises

​Example

​delete()

​Returns

​remove_column()

​Parameters

​Returns

​Raises

​publish()

​Parameters

​Returns

​Raises

​Example

​publish_stream()

​Parameters

​Returns

​Raises

​Example

​publish_batch()

​Parameters

​Returns

​Raises

​Example

​add_artifact()

​Parameters

​Returns

​update_artifact()

​Parameters

​Returns

​download_artifact()

​Parameters

​add_surrogate()

​Parameters

​Returns

​update_surrogate()

Parameters

Examples

download_data()

Parameters

Returns

Raises

Example

classmethod get()

Parameters

Returns

Raises

Example

classmethod from_name()

Parameters

Returns

Raises

Example

create()

Returns

Raises

Example

update()

add_column()

Parameters

Raises

Example

classmethod list()

Parameters

Yields

Returns

Example

duplicate()

Parameters

Returns

property datasets

Yields

Example

property baselines

Yields

Example

property deployment

Returns

classmethod from_data()

Parameters

Returns

Raises

Example

delete()

Returns

remove_column()

Parameters

Returns

Raises

publish()

Parameters

Returns

Raises

Example

publish_stream()

Parameters

Returns

Raises

Example

publish_batch()

Parameters

Returns

Raises

Example

add_artifact()

Parameters

Returns

update_artifact()

Parameters

Returns

download_artifact()

Parameters

add_surrogate()

Parameters

Returns

update_surrogate()