Publishing Inference Data

Publish Inference Events to Fiddler

After you onboard an ML model or LLM application as a Fiddler Model, you can publish inference events for analysis, performance monitoring, and reporting. There are two types of inference data:

• Pre-production data: Static datasets such as training or testing data that serve as Baseline references for comparison

• Production data: Time series data from live model inferences that Fiddler monitors against your baselines

Integration Methods

Fiddler offers two ways to publish inference data:

Python Client Library

Use the Python client for Python environments. Publish both production and pre-production inference data with the Model.publish() method.

For more details, see the Python client documentation.

REST API

Use the REST API for language-agnostic integration across any platform. Both production and pre-production inference data use a common interface.

For more details, see the Events REST API Guide.

Publish Pre-Production Data

Publish pre-production data to Fiddler as a single dataset. You can add multiple baseline datasets to a model to create customized references for different metrics and alert rules.

Fiddler accepts pre-production data in these formats:

• Pandas DataFrame

• Parquet file

• CSV file

For detailed instructions, see the Creating a Baseline guide.

Note:

Pre-production datasets are immutable after publication. You can't update them or delete individual rows.

Upload a Static Pre-Production Baseline

dataset_file_path = 'path_to_your_data.parquet'
dataset_name = 'a_unique_identifying_name'

project = fdl.Project.from_name(name='your_project_name')
model = fdl.Model.from_name(name='your_model_name', project_id=project.id)

job = model.publish(
    source=dataset_file_path,
    environment=fdl.EnvType.PRE_PRODUCTION,
    dataset_name=dataset_name,
)
# The publish() method is asynchronous. Use the publish job's wait() method 
# if synchronous behavior is desired.
# job.wait() 

Publish Production Data

Fiddler provides several methods for publishing and editing production inference data:

• Batch publishing: Send data in batches using pandas DataFrames, Parquet files, or CSV files

• Stream publishing: Send individual events or small batches in near real-time

• Update publishing: Modify previously published data

• Delete publishing: Remove published data when needed

Fiddler accepts production data in these formats:

• Pandas DataFrames

• Parquet files

• CSV files

• List of Python dictionaries (limited to stream and updates)

A list of dictionaries is an additional data format on top of the three common to pre-production and production data.

Choose the method that best fits your use case by reviewing the publishing guides below:

• Publish batch events

• Stream live events

• Update published events

• Delete events

• Publish ranking events

Key Considerations

Here are some considerations to keep in mind as you onboard models and begin publishing production data to Fiddler.

Inference Event Unique Identifier

Fiddler requires a unique identifier on each event published should you later need to update ground truth labels and/or metadata columns.

• Define the unique identifier column name when onboarding a model: Model.event_id_col

• A unique index on the event id column is not enforced

• As duplicate values are allowed, events sharing the same event id value will all be used in calculating metrics

Inference Event Timestamp

Fiddler requires a timestamp for each inference event which is used as the event occurrence timestamp in time-series monitoring charts and alert rule evaluation.

• Define the timestamp column name when onboarding a model: Model.event_ts_col

• If not defined, Fiddler will use the time of publication as the event occurrence timestamp

• Timestamps are stored and rendered in UTC

• Timestamps with timezone are accepted but will be converted to UTC

• Fiddler supports basic pandas timestamp formats by inferring from the data

Data Retention Policy

Fiddler retains production inference event data for 90 days. Contact your Fiddler customer success representative if you need a different retention period.

Raw Event Data

• Retained for 90 days from publication date

• Automatically deleted after 90 days

• Policy applies globally

Pre-Calculated Metrics

• Standard metrics derived from raw data are retained indefinitely

• Dashboards and charts continue to display historical trends after raw data expires

Runtime features

• Custom metrics require raw event data and aren't available for data older than 90 days

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