Add DataRobot Serverless prediction environments¶

On the Prediction Environments page, you can review the DataRobot prediction environments available to you and create DataRobot Serverless prediction environments to make scalable predictions with configurable compute instance settings.

To add a DataRobot serverless prediction environment:

1. Click Deployments > Prediction Environments and then click + Add prediction environment.

   

2. In the Add prediction environment dialog box, complete the following fields:

   

   Field	Description
   Name	Enter a descriptive name for the prediction environment.
   Description	(Optional) Enter a description of the external prediction environment.
   Platform	Select DataRobot Serverless.
   Batch jobs
   Max Concurrent Jobs	Decrease the maximum number of concurrent jobs for this serverless environment from the organization's defined maximum.
   Priority	Set the importance of batch jobs on this environment.

   How is the maximum concurrent job limit defined?

   There are two limits on max concurrent jobs and these limits depend on the details of your DataRobot installation. Each batch job is subject to both limits, meaning that the conditions of both must be satisfied for a batch job to run on the prediction environment. The first limit is the organization-level limit (default of 30 for Self-Managed installations or 10 for SaaS) defined by an organization administrator; this should be the higher limit. The second limit is the environment-level limit defined here by the prediction environment creator; this limit should be lower than the organization-level limit.

3. Once you configure the environment settings, click Add environment.

The environment is now available from the Prediction Environments page.

Deploy a model to the DataRobot serverless prediction environment¶

Using the pre-provisioned DataRobot Serverless environment, or a serverless environment you created, you can deploy a model to make predictions.

To deploy a model to the DataRobot serverless prediction environment:

1. On the Prediction Environments page, in the Platform row, locate the DataRobot Serverless prediction environments, and click the environment you want to deploy a model to.

   

2. On the Details tab, under Usages, in the Deployment column, click + Add new deployment.

   

3. In the Select model version from the registry dialog box, enter the name of the model you want to deploy in the Search box, click the model, and then click the DataRobot model version you want to deploy.

4. Click Select model version and then configure the deployment settings.

5. To configure on-demand predictions on this environment, click Show advanced options, scroll down to Advanced Predictions Configuration, and set the following Autoscaling options:

   

   Field	Description
   Minimum compute instances	(Premium feature) Set the minimum compute instances for the model deployment. If your organization doesn't have access to "always-on" predictions, this setting is set to 0 and isn't configurable. With the minimum compute instances set to 0, the inference server will be stopped after an inactivity period of 7 days. The minimum and maximum compute instances depend on the model type. For more information, see the compute instance configurations note.
   Maximum compute instances	Set the maximum compute instances for the model deployment to a value above the current configured minimum. To limit compute resource usage, set the maximum value equal to the minimum. The minimum and maximum compute instances depend on the model type. For more information, see the compute instance configurations note.

   Premium feature: Always-on predictions

   Always-on predictions are a premium feature. Deployment autoscaling management is required to configure the minimum compute instances setting. Contact your DataRobot representative or administrator for information on enabling the feature.

   Feature flag: Enable Deployment Auto-Scaling Management

   Compute instance configurations

   For DataRobot model deployments:

   • The default minimum is 0 and default maximum is 3.
   • The minimum and maximum limits are taken from the organization's max_compute_serverless_prediction_api setting.

   For custom model deployments:

   • The default minimum is 0 and default maximum is 1.
   • The minimum and maximum limits are taken from the organization's max_custom_model_replicas_per_deployment setting.
   • The minimum is always greater than 1 when running on GPUs (for LLMs).

   Update compute instances settings

   If, after deployment, you need to update the number of compute instances available to the model, you can change these settings on the Predictions Settings tab.

6. Click Deploy model.

   Depending on the availability of compute resources, it can take a few minutes after deployment for a prediction environment to be available for predictions.

Make predictions¶

After you've created a DataRobot serverless environment and deployed a model to that environment you can make real-time or batch predictions.