# Use ML with Athena syntax
<a name="ml-syntax"></a>

The `USING EXTERNAL FUNCTION` clause specifies an ML with Athena function or multiple functions that can be referenced by a subsequent `SELECT` statement in the query. You define the function name, variable names, and data types for the variables and return values.

## Synopsis
<a name="ml-synopsis"></a>

The following syntax shows a `USING EXTERNAL FUNCTION` clause that specifies an ML with Athena function.

```
USING EXTERNAL FUNCTION {{ml_function_name}} ({{variable1}} {{data_type}}[, {{variable2}} {{data_type}}][,...])
RETURNS {{data_type}} 
SAGEMAKER '{{sagemaker_endpoint}}'
SELECT {{ml_function_name}}()
```

## Parameters
<a name="udf-parameters"></a>

**USING EXTERNAL FUNCTION {{ml\_function\_name}} ({{variable1}} {{data\_type}}[, {{variable2}} {{data\_type}}][,...])**  
{{ml\_function\_name}} defines the function name, which can be used in subsequent query clauses. Each {{variable data\_type}} specifies a named variable and its corresponding data type that the SageMaker AI model accepts as input. The data type specified must be a supported Athena data type.

**RETURNS {{data\_type}}**  
{{data\_type}} specifies the SQL data type that {{ml\_function\_name}} returns to the query as output from the SageMaker AI model.

**SAGEMAKER '{{sagemaker\_endpoint}}'**  
{{sagemaker\_endpoint}} specifies the endpoint of the SageMaker AI model.

**SELECT [...] {{ml\_function\_name}}({{expression}}) [...]**  
The SELECT query that passes values to function variables and the SageMaker AI model to return a result. {{ml\_function\_name}} specifies the function defined earlier in the query, followed by an {{expression}} that is evaluated to pass values. Values that are passed and returned must match the corresponding data types specified for the function in the `USING EXTERNAL FUNCTION` clause.

## Example
<a name="ml-examples"></a>

The following example demonstrates a query using ML with Athena.

**Example**  

```
USING EXTERNAL FUNCTION predict_customer_registration(age INTEGER) 
    RETURNS DOUBLE
    SAGEMAKER 'xgboost-2019-09-20-04-49-29-303' 
SELECT predict_customer_registration(age) AS probability_of_enrolling, customer_id 
     FROM "sampledb"."ml_test_dataset" 
     WHERE predict_customer_registration(age) < 0.5;
```