---
title: Model Registry
description: How model packages are created and added to the Model Registry, manually or automatically. The Model Registry is an archive of your model packages where you can deploy and share packages.

---

# Model Registry {: #model-registry }

The Model Registry is an organizational hub for the variety of models used in DataRobot. Models are registered as deployment-ready model packages. Each registered model package functions the same way, regardless of the origin of its model. These model packages are grouped into _registered models_ containing _registered model versions_, allowing you to categorize them based on the business problem they solve. Registered models can contain the following artifacts as registered model versions:

* DataRobot, custom, and external models

* Challenger models (alongside the champion)

* Automatically retrained models.

 Custom models are [created and tested](custom-inf-model) in the Model Registry on the [Custom Model Workshop tab](custom-model-workshop/index), and external models [operate outside of DataRobot](mlops-agent/index), associated with an [external model](reg-external-models) in the Model Registry.

Once you add registered models, you can search, filter, and sort them. You can also share your registered models (and the versions they contain) with other users. Registered model packages (model artifacts with associated metadata) are listed on the **Model Registry > Registered Models** tab:

![](images/reg-models-page.png)

In addition, from the Model Registry, you can [generate model compliance documentation from model packages](reg-compliance) and [deploy, share, or archive models](reg-action).

## Add registered models and versions {: #add-registered-models-and-versions }

You can register DataRobot, custom, and external model packages. When you add model packages to the **Registered Models** page, you can create a new registered model (version one) or save the model package as a new version of an existing registered model. Model packages added as versions of the same registered model _must_ have the same target type, target name, and, if applicable, target classes and time series settings. To learn to add registered models to the mode registry, review the following documentation:

Topic | Describes
------|-----------
[Register DataRobot models](dr-model-reg) | How to add a DataRobot model to the Model Registry from the Leaderboard.
[Register custom models](reg-custom-models) <br> _(MLOps only)_  | How to register custom inference models in the Model Registry.
[Register external models](reg-external-models) <br> _(MLOps only)_ | How to register external models in the Model Registry.

!!! important
    Each registered model on the **Registered Models** page _must_ have a unique name. If you choose a name that exists anywhere within your organization when creating a new registered model, the **Model registration failed** warning appears. Use a different name or add this model as a new version of the existing registered model.

## Access registered models and versions {: #access-registered-models-and-versions }

On the **Registered Models** page, you can sort registered models by **Name** or **Last modified**. In a registered model, on the **Versions** tab, you can sort versions by **Name**, **Created at**, **Last updated at**, or **Model type**:

![](images/sort-reg-models.png)

In the top-left corner of the **Registered Models** page, you can click **Search** and enter the registered model name to locate it on the **Registered Models** page or click **Filters** to enable, modify, or clear filters on the **Registered Models** page:

![](images/search-filter-reg-models.png)

For more information, see the [View and manage registered models](reg-action) documentation.