# Fast ML Preset

{% hint style="danger" %}
***This synthesizer is deprecated.*** Please use the [GaussianCopulaSynthesizer](https://docs.sdv.dev/sdv/single-table-data/modeling/synthesizers/gaussiancopulasynthesizer) instead. The Gaussian Copula is just as fast as the Fast ML Preset, with more customization options for higher quality data.
{% endhint %}

The Fast ML Preset synthesizer is optimized for modeling speed. This is a great choice for first time SDV users. Use it to quickly get started with synthetic data.

```python
from sdv.lite import SingleTablePreset

synthesizer = SingleTablePreset(metadata, name='FAST_ML')
synthesizer.fit(data)

synthetic_data = synthesizer.sample(num_rows=10)
```

## Creating a synthesizer

When creating your synthesizer, you are required to pass in a [Metadata](https://docs.sdv.dev/sdv/single-table-data/data-preparation/creating-metadata) object as the first argument and the `'FAST_ML'` preset name as the second. All other parameters are optional. You can include them to customize the synthesizer.

```python
synthesizer = SingleTablePreset(
    metadata, # required
    name='FAST_ML', # required
    locales=['en_US', 'en_CA']
)
```

### Parameter Reference

**`locales`**: A list of locale strings. Any PII columns will correspond to the locales that you provide.

<table data-header-hidden><thead><tr><th width="218"></th><th></th></tr></thead><tbody><tr><td>(default) <code>['en_US']</code></td><td>Generate PII values in English corresponding to US-based concepts (eg. addresses, phone numbers, etc.)</td></tr><tr><td><code>&#x3C;list></code></td><td><p>Create data from the list of locales. Each locale string consists of a 2-character code for the language and 2-character code for the country, separated by an underscore.</p><p></p><p>For example <code>[</code><a href="https://faker.readthedocs.io/en/master/locales/en_US.html"><code>"en_US"</code></a><code>,</code> <a href="https://faker.readthedocs.io/en/master/locales/fr_CA.html"><code>"fr_CA"</code></a><code>]</code>. </p><p>For all options, see the <a href="https://faker.readthedocs.io/en/master/locales.html">Faker docs</a>.</p></td></tr></tbody></table>

## Learning from your data

To learn a machine learning model based on your real data, use the `fit` method.

### fit

**Parameters**

* (required) `data`: A [pandas DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) object containing the real data that the machine learning model will learn from

**Output** (None)

```python
synthesizer.fit(data)
```

{% hint style="info" %}
**Technical Details**: This preset uses the [GaussianCopulaSynthesizer](https://docs.sdv.dev/sdv/single-table-data/modeling/synthesizers/gaussiancopulasynthesizer) with fixed settings.

```python
GaussianCopulaSynthesizer(
    enforce_min_max_values=True,
    enforce_rounding=True,
    default_distribution='norm',
)
```

This allows for a fast modeling time while still using machine learning to learn patterns.
{% endhint %}

## Saving your synthesizer

Save your trained synthesizer for future use.

### save

Use this function to save your trained synthesizer as a Python pickle file.

**Parameters**

* (required) `filepath`: A string describing the filepath where you want to save your synthesizer. Make sure this ends in `.pkl`

**Output** (None) The file will be saved at the desired location

```python
synthesizer.save(
    filepath='my_synthesizer.pkl'
)
```

### SingleTablePreset.load

Use this function to load a trained synthesizer from a Python pickle file

**Parameters**

* (required) `filepath`: A string describing the filepath of your saved synthesizer

**Output** Your synthesizer, as a `SingleTablePreset` object

```python
from sdv.lite import SingleTablePreset

synthesizer = SingleTablePreset.load(
    filepath='my_synthesizer.pkl'
)
```

## What's next?

After training your synthesizer, you can now sample synthetic data. See the [Sampling](https://docs.sdv.dev/sdv/single-table-data/sampling) section for more details.