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You can stream production data to Fiddler as an alternative to batch publishing. Streaming offers lower latency, which is beneficial for high-velocity or near real-time models.

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When to Use Streaming vs. Batch Publishing

  • Use Model.publish_stream() when low latency is a priority and you’re working with individual events or small batches
  • Use Model.publish_batch() for large datasets or when you need to track longer-running processes with Job objects

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Stream Individual Inference Events

To stream a single inference event: 
project = fdl.Project.from_name(name='your_project_name')
model = fdl.Model.from_name(name='your_model_name', project_id=project.id)

# A single event must still be passed as a list.
model.publish_stream(events=[
    {
        'customer_id': 1234,
        'timestamp': 1710428785,
        'CreditScore': 650,
        'Geography': 'France',
        'Gender': 'Female',
        'Age': 45,
        'Tenure': 2,
        'Balance': 10000.0,
        'NumOfProducts': 1,
        'HasCrCard': 'Yes',
        'isActiveMember': 'Yes',
        'EstimatedSalary': 120000,
        'probability_churned': 0.105,
        'churn': 1
    }
])

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Stream Small Batches of Events

For better efficiency, you can stream multiple events at once: 
# For multiple events, where `my_events` is a list of Python dictionaries
model.publish_stream(events=my_events)
🚧 Note Convert a pandas DataFrame to a list of event dictionaries using the to_dict function. 
my_events = my_df.to_dict(orient='records')
For batches larger than 5,000 events, prefer Model.publish_batch() over streaming.