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Serve declarative features

Beta

This feature is in Beta. Workspace admins can control access to this feature from the Previews page. See Manage Databricks previews.

important

Feature Serving endpoints are not supported for Declarative Feature Engineering. To serve features online, deploy a model serving endpoint using a model logged through Unity Catalog.

Models that are trained using features from Databricks automatically track lineage to the features they were trained on. When deployed as model serving endpoints, these models use Unity Catalog to look up features from online stores.

Deploy a model serving endpoint​

Use an existing model serving endpoint, or use the Databricks SDK to create a new one. The model must be registered in Unity Catalog.

The following code shows how to create a new model serving endpoint. For more information, see Create custom model serving endpoints.

Python
from databricks.sdk import WorkspaceClient
from databricks.sdk.service.serving import EndpointCoreConfigInput, ServedEntityInput

w = WorkspaceClient()

endpoint_name = "fraud-detection-endpoint"
model_name = "main.ecommerce.fraud_model"

w.serving_endpoints.create(
    name=endpoint_name,
    config=EndpointCoreConfigInput(
        name=endpoint_name,
        served_entities=[
            ServedEntityInput(
                entity_name=model_name,
                entity_version=1,
                max_provisioned_concurrency=4,
                min_provisioned_concurrency=0,
            )
        ],
    ),
)

Query the endpoint​

Python
from databricks.sdk import WorkspaceClient

w = WorkspaceClient()

response = w.serving_endpoints.query(
    name="fraud-detection-endpoint",
    dataframe_records=[
        {"user_id": "user_123", "transaction_time": "2026-03-01T12:00:00"},
    ],
)

Query the endpoint with RequestSource features​

If the model was trained with RequestSource features, the request payload must also include all RequestSource columns. These columns were added to the MLflow model signature during log_model, so the endpoint's API schema reflects the required request fields.

Python
response = w.serving_endpoints.query(
    name="fraud-detection-endpoint",
    dataframe_records=[
        {
            "user_id": "user_123",
            "transaction_time": "2026-03-01T12:00:00",
            "transaction_amount": 275.30,  # RequestSource column
            "vendor_id": "v_42",           # RequestSource column (also used as entity key)
        },
    ],
)

Entity keys are used for looking up table-backed features from the online store. RequestSource columns are passed through directly to the model.

You can also use curl:

Bash
curl -X POST "https://<workspace>.cloud.databricks.com/serving-endpoints/<endpoint>/invocations" \
  -H "Authorization: Bearer $DATABRICKS_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "dataframe_records": [
      {
        "user_id": "user_123",
        "transaction_time": "2026-03-01T12:00:00",
        "transaction_amount": 275.30,
        "vendor_id": "v_42"
      }
    ]
  }'
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