> For the complete documentation index, see [llms.txt](https://docs.seldon.ai/seldon-enterprise-platform/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.seldon.ai/seldon-enterprise-platform/product-tour/deployment-wizard.md).

# Deployment Wizard

The **Deployment Wizard** guides you through setting up and configuring new deployments.

The `Overview` button on the top-left of your screen will take you to the list of current deployments, where the deployment wizard can be opened by clicking on the `Create new deployment` button on the top-right.

!["Create new deployment" button in the Overview page](/files/8f0SL7XgVdAwjcElf1iL)

## Deployment Details

In the **Deployment Details** page, you need to choose a `Name` and `Namespace` for your deployment. You also need to choose the deployment `Type`, which affects the following sections of this wizard.

![Seldon Deployment details](/files/pQqfYoORKgzlQBSuIYhw)

{% hint style="info" %}
**Note**: Deployments of type `Seldon Deployment` use Seldon Core v1, while those of type `Seldon ML Pipeline` use Seldon Core v2. See the [feature comparison between Core v1 and Core v2](https://docs.seldon.io/projects/seldon-core/en/v2/contents/about/index.html) for details.
{% endhint %}

## Default Predictor

In the **Default Predictor** page, you will need to specify your model's `Runtime` and the `Model URI` where its artifacts are stored. The `Runtime` field is pre-filled with supported runtimes, and also includes options for `Custom` runtime configurations.

You can optionally provide a `Storage Secret` for private model artifacts. See [Secrets Management ](/seldon-enterprise-platform/operations/secrets-management.md)for details.

You can also change the `Model Project` from the `default` project. Projects are a logical grouping of resources for[ authorization purposes](/seldon-enterprise-platform/operations/authorization.md).

![Deployment predictor details](/files/NBfJv5WhKndpbMMw5gBi)

### Configuring Custom Models

In addition to pre-packaged runtimes, custom container images or custom inference artifacts can be configured in a similar manner.

#### Seldon Core v1

For deployments of type `Seldon Deployment`, you can choose the `Custom` runtime and specify the URI for your Docker image instead of a model artifact URI.

![Core 1 Deployment custom runtime](/files/e6uXrcthZVQ0Fi1FMdbD)

#### Seldon Core v2

For deployments of type `Seldon ML Pipeline`, you can select a custom inference server for your model by specifying `Model Requirements` as follows. Custom inference servers need to be set up manually, not through the **Deployment Wizard**.

See [Servers in Seldon Core v2](https://docs.seldon.io/projects/seldon-core/en/v2/contents/servers/index.html) documentation for how to configure servers and models.

![Core 2 Deployment custom inference server](/files/Iggl7mUBQg9VfbUO1DQ8)

## Optional Configurations

In the following pages, you can set optional configurations such as resource requirements, auto-scaling, and version comments for GitOps.

For deployments of type `Seldon Deployment`, you can additionally configure environment variables, inference request logging, and input/output transformers.

## Deployment Summary

Before creating the deployment, you can review its Kubernetes manifest in the **Launch Deployment** page. Click on the `LAUNCH` button to proceed.

![Kubernetes manifest](/files/owh7evcaDQi12UqywtiN)