# Streaming Live Events

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.

### When to Use Streaming vs. Batch Publishing

* Use streaming when low latency is a priority and you're working with individual events or small batches
* Use batch publishing for large datasets or when you need to track longer-running processes with Job objects

#### Stream Individual Inference Events

To stream a single inference event:

```python
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 an array.
model.publish([
    {
        '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
    }
])
```

#### Stream Small Batches of Events

For better efficiency, you can stream multiple events at once:

```python
# For multiple events, where `my_events` is a list of Python dictionaries
model.publish(my_events)
```

> 🚧 Note
>
> Convert a pandas DataFrame to a list of event dictionaries using the to\_dict function.

```python
my_events = my_df.to_dict(orient='records')
```

For batches larger than 5,000 events, prefer [batch publishing](/developers/client-library-reference/publishing-production-data/publishing-batches-of-events.md) over streaming.


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