> ## Documentation Index
> Fetch the complete documentation index at: https://anaconda.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Training models

Data Science & AI Workbench provides machine learning libraries such as scikit-learn and Tensorflow that you can use to train the models you create.

## To train a model

When you are ready to run an algorithm against your model and tune it, download the `scikit-learn` or `Tensorflow` package from the `anaconda` channel. If you don’t see this channel or these packages in your **Channels** list, contact your Administrator to [mirror these packages](https://anaconda.com/docs/data-science/latest/admin/chan-pkg/mirror.md) to make them available to you.

## Serializing your model

When you are ready to convert your model or application into a format that can be easily distributed and reconstructed by others, use Workbench to [deploy it](https://anaconda.com/docs/data-science/latest/data-science-workflows/ml-deep-learn/deploy-model.md).

* YAML: supports non-hierarchical data structures & scalar data
* JSON: for client-server communication in web apps
* HD5: designed to store large amounts of hierarchical data; works well for time series data (stored in arrays)

> [!NOTE]
>
> Your model or app must be written in a programming language that supports object serialization, such as Python, PHP, R or Java.