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# Overview

Deployment makes a trained model available for inference and explanations.

Start from a model created by training, choose a deployment configuration, then create a deployed model. Once the deployed model is running, it can be queried and inspected through deployed model views.

## How deployment works

Deployment starts from a trained model.

A deployment configuration defines how the model should be served.

Deploying the model creates a deployed model resource that can be monitored and managed.

When the deployed model is running, it can be used for inference and explanation workflows.

## Deployment types

The platform supports two deployment types.

| Type         | Use                                                                                         |
| ------------ | ------------------------------------------------------------------------------------------- |
| `SERVERLESS` | Use for on-demand workloads where scaling to zero is useful and cold starts are acceptable. |
| `REALTIME`   | Use for low-latency workloads where the model should stay ready for requests.               |

Serverless configurations use memory size and maximum concurrency. Real-time configurations use a serving machine type and machine count.

## Deployed model lifecycle

A deployed model is created from a model and a deployment configuration.

Use the deployed model resource to monitor deployment status, rename the deployment, delete it when it is no longer needed, and access deployed model views.