在Spark UDF函数中使用Sagemaker predictor

import org.apache.spark.ml.linalg.Vector
import org.apache.spark.sql.functions.udf
import com.amazonaws.services.sagemaker.runtime.AmazonSageMakerRuntimeClientBuilder
import com.amazonaws.services.sagemakerruntime.model.InvokeEndpointRequest

val sagemakerEndpoint = "your_sagemaker_endpoint"
val awsRegion = "your_aws_region"

val sagemakerPredictor = AmazonSageMakerRuntimeClientBuilder.standard()
  .withEndpointConfiguration(new AwsClientBuilder.EndpointConfiguration(sagemakerEndpoint, awsRegion))
  .build()

val predictUDF = udf((features: Vector) => {
  val payload = features.toArray.mkString(" ")
  val request = new InvokeEndpointRequest()
    .withEndpointName("your_sagemaker_endpoint_name")
    .withContentType("text/csv")
    .withBody(payload)

  val response = sagemakerPredictor.invokeEndpoint(request)
  val result = response.getBody
  result
})

val data = spark.read.csv("your_data.csv")
val predictedData = data.withColumn("prediction", predictUDF($"features"))