# configure prediction
The `configure prediction` command configures a prediction deployment. It is used to deploy a registered ML model for serving. In the notebook, you will be guided through a template form that will prompt you for various inputs, such as the prediction deployment name, the registered ML model name, and the model version.
## Syntax
```
configure prediction
```
## Parameters
1. Select **New** from the **Prediction** drop-down list.
2. Specify a prediction deployment name in the **Prediction Name** field.
3. Select a registered model from the **Model Name** drop-down list.
4. Select the model version from the **Model Version** drop-down list.
5. Select the source type from the **Source Type** drop-down list:
* Select **http** for a REST source.
* Select **datasource** or **dataset** for a batch source, and then select the name of the data source or dataset from the **Source Name** drop-down list.
6. Select the **Destination Type** for the prediction response:
* Select **http** for an http response.
* Select **datasource** to store the results in the location specified by the data-source definition.
7. Select the **Advanced Settings** checkbox to specify the file name of a custom preprocessor Python module.
Before starting the prediction job, make sure to install this preprocessor module using the install preprocessor command.
8. Click the **Save Configuration** button to save the prediction deployment.
## Example
