# Deploy custom models
URL: https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/deploy-custom-inf-model.html

> How to deploy custom models, pre-trained models assembled in the Custom Model Workshop.

# Deploy custom models

After you [create a custom inference model](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-inf-model.html) using the Custom Model Workshop, you can deploy it to a [custom model environment](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-environments/custom-environments.html).

> [!NOTE] Note
> While you can deploy your custom inference model to an environment without testing, DataRobot strongly recommends your model pass testing before deployment.

## Register and deploy a custom model

To deploy an unregistered custom model:

1. Navigate toModel Registry > Custom Model Workshop > Modelsand select the model you want to deploy.
2. On theAssembletab, clickRegister to deployin the middle of the page. NoteDataRobot recommends testing that your model can make predictions before deploying.
3. In theRegister new modeldialog box, configure the following: FieldDescriptionRegister modelSelect one of the following:Register new model:Create a new registered model. This creates the first version (V1).Save as a new version to existing model:Create a version of an existing registered model. This increments the version number and adds a new version to the registered model.Registered model name / Registered ModelDo one of the following:Registered model name:Enter a unique and descriptive name for the new registered model. If you choose a name that exists anywhere within your organization, theModel registration failederror message appears.Registered Model:Select the existing registered model you want to add a new version to.Registered model versionAssigned automatically. This displays the expected version number of the version (e.g., V1, V2, V3) you create. This is alwaysV1when you selectRegister a new model.Optional settingsVersion descriptionDescribe the business problem these model packages solve, or, more generally, the relationship between them.TagsClick+ Add itemand enter aKeyand aValuefor each key-value pair you want to tag the modelversionwith. Tags do not apply to the registered model, just the versions within. Tags added when registering a new model are applied toV1.
4. ClickAdd to Registry. The model opens on theModel Registry > Registered Modelstab.
5. In the registered model version header, clickDeploy, and thenconfigure the deployment settings. Most information for your custom model is provided automatically.
6. ClickDeploy model.

## Deploy a registered custom model

To deploy a registered custom model:

1. On theRegistered Modelspage, click the registered model containing the model version you want to deploy.
2. To open the registered model version, do either of the following:
3. In the version header, clickDeploy, and thenconfigure the deployment settings.
4. ClickDeploy model. TheCreating deploymentmodal appears, tracking the status of the deployment creation process, including the application of deployment settings and the calculation of the drift baseline. You canReturn to deploymentsor monitor the deployment progress from the modal, allowing you to access theCheck deployment's MLOps logslink if an error occurs:

### Make predictions

Once a custom inference model is deployed, it can make predictions using API calls to a dedicated prediction server managed by DataRobot. You can find more information about [using the prediction API](https://docs.datarobot.com/en/docs/api/reference/predapi/legacy-predapi/dr-predapi.html) in the Predictions documentation.

> [!NOTE] Training dataset considerations
> When making predictions through a deployed model, the prediction dataset is handled as follows:
> 
> Without
> training data, only the target feature is removed from the prediction dataset.
> With
> training data, any features not in the training dataset are removed from the prediction dataset.

### Deployment status

When DataRobot deploys a custom model, a Launching badge appears under the deployment name in the [deployment inventory](https://docs.datarobot.com/en/docs/classic-ui/mlops/manage-mlops/deploy-inventory.html), and on any tab within the deployment. The following deployment status values are available for custom model deployments:

| Status | Badge |
| --- | --- |
|  | The custom model deployment process is still in progress. You can't currently make predictions through this deployment, or access deployment tabs that require an active deployment. |
|  | The custom model deployment process completed with errors. You may be unable to make predictions through this deployment; however, if you deactivate this deployment, you can't reactivate it until you resolve the deployment errors. You should check the MLOps Logs to troubleshoot the custom model deployment. |
|  | The custom model deployment process failed and the deployment is Inactive. You can't currently make predictions through this deployment, or access deployment tabs that require an active deployment. You should check the MLOps Logs to troubleshoot the custom model deployment. |

From a deployment with an Errored or Warning status, you can access the Service Health MLOps logs link from the warning on any tab. This link takes you directly to the Service Health tab:

On the Service Health tab, under Recent Activity, you can click the MLOps Logs tab to view the Event Details. In the Event Details, you can click View logs to access the [custom model deployment logs](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/deploy-methods/deploy-custom-inf-model.html#deployment-logs) to diagnose the cause of the error:

### Deployment logs

When you deploy a custom model, it generates log reports unique to this type of deployment, allowing you to debug custom code and troubleshoot prediction request failures from within DataRobot.

To view the logs for a deployed model, navigate to the deployment, open the Actions menu, and select View Logs.

You can access two types of logs:

- Runtime Logsare used to troubleshoot failed prediction requests (via thePredictionstab or the API). The logs are captured from the Docker container running the deployed custom model and contain up to 1MB of data. The logs are cached for 5 minutes after you make a prediction request. You can re-request the logs by clickingRefresh.
- Deployment logsare automatically captured if the custom model fails while deploying. The logs are stored permanently as part of the deployment.

> [!NOTE] Note
> DataRobot only provides logs from inside the Docker container from which the custom model runs. Therefore, it is possible in cases where a custom model fails to deploy or fails to execute a prediction request that no logs will be available. This is because the failures occurred outside of the Docker container.

Use the Search bar to find specific references within the logs. Click Download Log to save a local copy of the logs.

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