# Create a Model

## Creating a Model

A Model represents a machine learning model. Each Model has a type. Currently Merlin supports both standard model types (PyTorch, SKLearn, Tensorflow, and XGBoost) and user-defined models (PyFunc model).

Merlin also supports custom models. More info can be found here:

{% content-ref url="/pages/6kwE95Lt6Vke46x0PTxu" %}
[Custom Model](/user-guides/01_getting_started/02_creating_a_model/01_custom_model.md)
{% endcontent-ref %}

Conceptually, a Model in Merlin is similar to a class in programming languages. To instantiate a Model, you’ll have to create a [Model Version](#creating-a-model-version).

`merlin.set_model(<model_name>, <model_type>)` will set the active model to the name given by parameter. If the Model with given name is not found, a new Model will be created.

{% code title="model\_creation.py" overflow="wrap" lineNumbers="true" %}

```python
import merlin
from merlin.model import ModelType

merlin.set_model("tensorflow-sample", ModelType.TENSORFLOW)
```

{% endcode %}

## Creating a Model Version

A Model Version represents a snapshot of A particular Model iteration. A Model Version might contain artifacts which are deployable to Merlin. You'll also be able to attach information such as metrics and tags to a given Model Version.

{% code title="model\_version\_creation.py" overflow="wrap" lineNumbers="true" %}

```python
with merlin.new_model_version() as v:
    merlin.log_metric("metric", 0.1)
    merlin.log_param("param", "value")
    merlin.set_tag("tag", "value")

    merlin.log_model(model_dir='tensorflow-sample')
```

{% endcode %}


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