Serving

Deployment makes a trained model available for inference: using input values to produce predictions. A running deployment also supplies the outputs used for evaluation and explanations.

A trained model stores the result of learning. A deployment configuration defines its serving resources. A deployed model combines the two into the resource that accepts queries.

How deployment works

Deployment types

Serving resources are separate from training and processing compute. Choose them for the model size and expected query workload; a training machine configuration cannot be substituted for a deployment configuration.

For models that use feature engineering, a running Feature Transform Service applies the required input transformations. Select it during deployment and keep it running while the deployment uses it.

Deployed model lifecycle

Under Serving > Deployed Models, inspect the deployment status and its model and configuration references. Use the deployed-model identity when evaluating or explaining predictions, so results can be traced to the intended trained model.

Deleting a deployment removes the serving resource and makes it unavailable for queries. Check whether it is still needed for evaluation or explanations before removing it.

Next steps

Create a deployment configuration, then deploy the model.