diff --git a/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala b/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala
index 3c7bcf7590e6..2a3837195df8 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala
@@ -25,6 +25,7 @@ import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.sql.DataFrame
 import org.apache.spark.sql.functions._
+import org.apache.spark.sql.UserDefinedFunction
 import org.apache.spark.sql.types._
 
 /**
@@ -90,7 +91,7 @@ abstract class UnaryTransformer[IN, OUT, T <: UnaryTransformer[IN, OUT, T]]
    * account of the embedded param map. So the param values should be determined solely by the input
    * param map.
    */
-  protected def createTransformFunc: IN => OUT
+  protected def transformFunc: UserDefinedFunction
 
   /**
    * Returns the data type of the output column.
@@ -115,8 +116,7 @@ abstract class UnaryTransformer[IN, OUT, T <: UnaryTransformer[IN, OUT, T]]
 
   override def transform(dataset: DataFrame): DataFrame = {
     transformSchema(dataset.schema, logging = true)
-    dataset.withColumn($(outputCol),
-      callUDF(this.createTransformFunc, outputDataType, dataset($(inputCol))))
+    dataset.withColumn($(outputCol), this.transformFunc(col($(inputCol))))
   }
 
   override def copy(extra: ParamMap): T = defaultCopy(extra)
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/DCT.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/DCT.scala
index a6f878151de7..70bade556c85 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/DCT.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/DCT.scala
@@ -24,6 +24,8 @@ import org.apache.spark.ml.UnaryTransformer
 import org.apache.spark.ml.param.BooleanParam
 import org.apache.spark.ml.util._
 import org.apache.spark.mllib.linalg.{Vector, Vectors, VectorUDT}
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.UserDefinedFunction
 import org.apache.spark.sql.types.DataType
 
 /**
@@ -57,11 +59,13 @@ class DCT(override val uid: String)
 
   setDefault(inverse -> false)
 
-  override protected def createTransformFunc: Vector => Vector = { vec =>
-    val result = vec.toArray
-    val jTransformer = new DoubleDCT_1D(result.length)
-    if ($(inverse)) jTransformer.inverse(result, true) else jTransformer.forward(result, true)
-    Vectors.dense(result)
+  override protected def transformFunc: UserDefinedFunction = {
+    udf { input: Vector =>
+      val result = input.toArray
+      val jTransformer = new DoubleDCT_1D(result.length)
+      if ($(inverse)) jTransformer.inverse(result, true) else jTransformer.forward(result, true)
+      Vectors.dense(result)
+    }
   }
 
   override protected def validateInputType(inputType: DataType): Unit = {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/ElementwiseProduct.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/ElementwiseProduct.scala
index 07a12df32035..63183dbc8d42 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/ElementwiseProduct.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/ElementwiseProduct.scala
@@ -23,6 +23,8 @@ import org.apache.spark.ml.param.{Param, ParamMap}
 import org.apache.spark.ml.util.Identifiable
 import org.apache.spark.mllib.feature
 import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.UserDefinedFunction
 import org.apache.spark.sql.types.DataType
 
 /**
@@ -49,10 +51,12 @@ class ElementwiseProduct(override val uid: String)
   /** @group getParam */
   def getScalingVec: Vector = getOrDefault(scalingVec)
 
-  override protected def createTransformFunc: Vector => Vector = {
+  override protected def transformFunc: UserDefinedFunction = {
     require(params.contains(scalingVec), s"transformation requires a weight vector")
-    val elemScaler = new feature.ElementwiseProduct($(scalingVec))
-    elemScaler.transform
+    udf { input: Vector =>
+      val elemScaler = new feature.ElementwiseProduct($(scalingVec))
+      elemScaler.transform(input)
+    }
   }
 
   override protected def outputDataType: DataType = new VectorUDT()
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/NGram.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/NGram.scala
index f8bc7e3f0c03..30b79241eace 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/NGram.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/NGram.scala
@@ -21,6 +21,8 @@ import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml.UnaryTransformer
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.util._
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.UserDefinedFunction
 import org.apache.spark.sql.types.{ArrayType, DataType, StringType}
 
 /**
@@ -56,8 +58,10 @@ class NGram(override val uid: String)
 
   setDefault(n -> 2)
 
-  override protected def createTransformFunc: Seq[String] => Seq[String] = {
-    _.iterator.sliding($(n)).withPartial(false).map(_.mkString(" ")).toSeq
+  override protected def transformFunc: UserDefinedFunction = {
+    udf { input: Seq[String] =>
+      input.iterator.sliding($(n)).withPartial(false).map(_.mkString(" ")).toSeq
+    }
   }
 
   override protected def validateInputType(inputType: DataType): Unit = {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala
index a603b3f83320..b74bb16f053d 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala
@@ -23,6 +23,8 @@ import org.apache.spark.ml.param.{DoubleParam, ParamValidators}
 import org.apache.spark.ml.util._
 import org.apache.spark.mllib.feature
 import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.UserDefinedFunction
 import org.apache.spark.sql.types.DataType
 
 /**
@@ -50,9 +52,11 @@ class Normalizer(override val uid: String)
   /** @group setParam */
   def setP(value: Double): this.type = set(p, value)
 
-  override protected def createTransformFunc: Vector => Vector = {
-    val normalizer = new feature.Normalizer($(p))
-    normalizer.transform
+  override protected def transformFunc: UserDefinedFunction = {
+    udf { input: Vector =>
+      val normalizer = new feature.Normalizer($(p))
+      normalizer.transform(input)
+    }
   }
 
   override protected def outputDataType: DataType = new VectorUDT()
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala
index 42b26c8ee836..49549c79e4c9 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala
@@ -24,7 +24,9 @@ import org.apache.spark.ml.UnaryTransformer
 import org.apache.spark.ml.param.{IntParam, ParamMap, ParamValidators}
 import org.apache.spark.ml.util._
 import org.apache.spark.mllib.linalg._
+import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types.DataType
+import org.apache.spark.sql.UserDefinedFunction
 
 /**
  * :: Experimental ::
@@ -56,8 +58,10 @@ class PolynomialExpansion(override val uid: String)
   /** @group setParam */
   def setDegree(value: Int): this.type = set(degree, value)
 
-  override protected def createTransformFunc: Vector => Vector = { v =>
-    PolynomialExpansion.expand(v, $(degree))
+  override protected def transformFunc: UserDefinedFunction = {
+    udf { input: Vector =>
+      PolynomialExpansion.expand(input, $(degree))
+    }
   }
 
   override protected def outputDataType: DataType = new VectorUDT()
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala
index 8456a0e91580..1eb457115b5c 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala
@@ -21,6 +21,8 @@ import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.ml.UnaryTransformer
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.util._
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.UserDefinedFunction
 import org.apache.spark.sql.types.{ArrayType, DataType, StringType}
 
 /**
@@ -35,8 +37,10 @@ class Tokenizer(override val uid: String)
 
   def this() = this(Identifiable.randomUID("tok"))
 
-  override protected def createTransformFunc: String => Seq[String] = {
-    _.toLowerCase.split("\\s")
+  override protected def transformFunc: UserDefinedFunction = {
+    udf { input: String =>
+      input.toLowerCase.split("\\s")
+    }
   }
 
   override protected def validateInputType(inputType: DataType): Unit = {
@@ -124,12 +128,14 @@ class RegexTokenizer(override val uid: String)
 
   setDefault(minTokenLength -> 1, gaps -> true, pattern -> "\\s+", toLowercase -> true)
 
-  override protected def createTransformFunc: String => Seq[String] = { originStr =>
-    val re = $(pattern).r
-    val str = if ($(toLowercase)) originStr.toLowerCase() else originStr
-    val tokens = if ($(gaps)) re.split(str).toSeq else re.findAllIn(str).toSeq
-    val minLength = $(minTokenLength)
-    tokens.filter(_.length >= minLength)
+  override protected def transformFunc: UserDefinedFunction = {
+    udf { input: String =>
+      val re = $(pattern).r
+      val str = if ($(toLowercase)) input.toLowerCase() else input
+      val tokens = if ($(gaps)) re.split(str).toSeq else re.findAllIn(str).toSeq
+      val minLength = $(minTokenLength)
+      tokens.filter(_.length >= minLength)
+    }
   }
 
   override protected def validateInputType(inputType: DataType): Unit = {