# Creating the monitoring model

We create a new model by using the `create` method. Where we can define things like how the data should be chunked, the key performance metric, etc. 

```python
# Create model
model = nml_sdk.monitoring.Model.create(
    name='Example model',
    schema=schema,
    chunk_period='MONTHLY',
    reference_data=reference_data,
    analysis_data=analysis_data,
    target_data=target_data,
    key_performance_metric='F1',
)
```

More info about the `Model` class can be found in its [API reference](https://nannyml.github.io/nannyml-cloud-sdk/api_reference/monitoring/model/).

In case you are wondering why we need to pass the `reference_data` twice —once in the schema and another in the model created— the reason is that both steps are treated differently. In the schema inspection step, we transmit only a few rows (100 to be precise) to the NannyML Cloud server for deriving the schema. While when creating the model, the entire thing is uploaded, so that will take a bit more time.


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