> ## 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.

# Deploying models as endpoints

Data Science & AI Workbench enables you to deploy machine learning models as endpoints to make them available to others, so the models can be [queried and scored](https://anaconda.com/docs/data-science/latest/data-science-workflows/ml-deep-learn/query-score-model.md). You can then save users’ input data as part of the training data, and retrain the model with the new training dataset.

## Versioning your model

To enable you to test variations of a model, you can [deploy multiple versions of the model](https://anaconda.com/docs/data-science/latest/data-science-workflows/deployments/main.md). You can then direct different sets of users to each of the versions, to facilitate A/B testing.

## Deploying your model as an endpoint

Deploying a model as an endpoint involves these simple steps:

1. [Create a project](https://anaconda.com/docs/data-science/latest/data-science-workflows/projects/main.md) to tell Workbench where to look for the artifacts that comprise the model.
2. [Deploy the project](https://anaconda.com/docs/data-science/latest/data-science-workflows/deployments/main.md) to build the model and all of its dependencies. Now you—and others with whom you [share the deployment](https://anaconda.com/docs/data-science/latest/data-science-workflows/deployments/share.md)—can interact with the app, and select different datasets and algorithms.