# EnvType

Environment types for data publishing in Fiddler.

This enum defines the two primary environment types used when publishing inference data to Fiddler. The environment type determines how Fiddler processes, stores, and monitors the data.

## PRODUCTION

Live inference data from production model deployments

## PRE\_PRODUCTION

Static baseline datasets for drift detection reference

### Examples

Publishing pre-production baseline data:

```python
# Upload training data as baseline
baseline_job = model.publish(
    source='training_data.csv',
    environment=fdl.EnvType.PRE_PRODUCTION,
    dataset_name='training_baseline'

    ## )
```

Publishing production inference data:

```python
# Stream live inference events
model.publish(
    source=inference_events,
    environment=fdl.EnvType.PRODUCTION

    ## )
```

Environment-specific data handling:

```python
# Different processing based on environment
if env_type == fdl.EnvType.PRE_PRODUCTION:

    # Static dataset - immutable after upload
    # Used for baseline calculations
    # No time-series monitoring
    pass

elif env_type == fdl.EnvType.PRODUCTION:
    # Time-series data - continuous monitoring
    # Compared against baselines for drift
    # Subject to data retention policies
    pass
```

{% hint style="info" %}
Environment types cannot be changed after data publication. Choose the appropriate environment based on your data's intended use case.
{% endhint %}

## PRODUCTION *= 'PRODUCTION'*

Production environment for live inference data.

Used for time-series inference data from live model deployments. This data:

* Gets monitored continuously for drift and performance issues
* Is compared against baseline datasets for anomaly detection
* Supports real-time streaming and batch publishing
* Is subject to data retention policies (typically 90 days)
* Enables alert rule evaluation and dashboard visualization

Typical use cases:

* Live model inference results
* Real-time prediction streaming
* Batch inference job outputs
* A/B testing data
* Production model monitoring

Data characteristics:

* Time-series with timestamps
* Continuous data flow
* Variable data volumes
* Monitored for drift patterns

## PRE\_PRODUCTION *= 'PRE\_PRODUCTION'*

Pre-production environment for baseline datasets.

Used for static datasets that serve as reference points for monitoring. This data:

* Remains immutable after publication
* Serves as baseline for drift detection calculations
* Represents expected model behavior and data distributions
* Is retained indefinitely for comparison purposes
* Does not appear in time-series monitoring charts

Typical use cases:

* Training dataset baselines
* Validation dataset references
* Historical "golden" datasets
* Model performance benchmarks
* Data distribution references

Data characteristics:

* Static, unchanging datasets
* Representative of expected distributions
* Used for statistical comparisons
* No time-series component


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