diff --git a/mllib/src/main/scala/org/apache/spark/ml/Predictor.scala b/mllib/src/main/scala/org/apache/spark/ml/Predictor.scala
index 333b42711ec5..19fe039b8fd0 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/Predictor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/Predictor.scala
@@ -169,10 +169,7 @@ abstract class PredictionModel[FeaturesType, M <: PredictionModel[FeaturesType,
   override def transform(dataset: DataFrame): DataFrame = {
     transformSchema(dataset.schema, logging = true)
     if ($(predictionCol).nonEmpty) {
-      val predictUDF = udf { (features: Any) =>
-        predict(features.asInstanceOf[FeaturesType])
-      }
-      dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
+      transformImpl(dataset)
     } else {
       this.logWarning(s"$uid: Predictor.transform() was called as NOOP" +
         " since no output columns were set.")
@@ -180,6 +177,13 @@ abstract class PredictionModel[FeaturesType, M <: PredictionModel[FeaturesType,
     }
   }
 
+  protected def transformImpl(dataset: DataFrame): DataFrame = {
+    val predictUDF = udf { (features: Any) =>
+      predict(features.asInstanceOf[FeaturesType])
+    }
+    dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
+  }
+
   /**
    * Predict label for the given features.
    * This internal method is used to implement [[transform()]] and output [[predictionCol]].
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
index 554e3b8e052b..eb0b1a0a405f 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
@@ -34,6 +34,8 @@ import org.apache.spark.mllib.tree.loss.{LogLoss => OldLogLoss, Loss => OldLoss}
 import org.apache.spark.mllib.tree.model.{GradientBoostedTreesModel => OldGBTModel}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types.DoubleType
 
 /**
  * :: Experimental ::
@@ -177,8 +179,15 @@ final class GBTClassificationModel(
 
   override def treeWeights: Array[Double] = _treeWeights
 
+  override protected def transformImpl(dataset: DataFrame): DataFrame = {
+    val bcastModel = dataset.sqlContext.sparkContext.broadcast(this)
+    val predictUDF = udf { (features: Any) =>
+      bcastModel.value.predict(features.asInstanceOf[Vector])
+    }
+    dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
+  }
+
   override protected def predict(features: Vector): Double = {
-    // TODO: Override transform() to broadcast model: SPARK-7127
     // TODO: When we add a generic Boosting class, handle transform there?  SPARK-7129
     // Classifies by thresholding sum of weighted tree predictions
     val treePredictions = _trees.map(_.rootNode.predict(features))
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
index d3c67494a31e..991b551c4338 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
@@ -31,6 +31,8 @@ import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
 import org.apache.spark.mllib.tree.model.{RandomForestModel => OldRandomForestModel}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types.DoubleType
 
 /**
  * :: Experimental ::
@@ -135,8 +137,15 @@ final class RandomForestClassificationModel private[ml] (
 
   override def treeWeights: Array[Double] = _treeWeights
 
+  override protected def transformImpl(dataset: DataFrame): DataFrame = {
+    val bcastModel = dataset.sqlContext.sparkContext.broadcast(this)
+    val predictUDF = udf { (features: Any) =>
+      bcastModel.value.predict(features.asInstanceOf[Vector])
+    }
+    dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
+  }
+
   override protected def predict(features: Vector): Double = {
-    // TODO: Override transform() to broadcast model.  SPARK-7127
     // TODO: When we add a generic Bagging class, handle transform there: SPARK-7128
     // Classifies using majority votes.
     // Ignore the weights since all are 1.0 for now.
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
index 47c110d027d6..e38dc73ee0ba 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
@@ -33,6 +33,8 @@ import org.apache.spark.mllib.tree.loss.{AbsoluteError => OldAbsoluteError, Loss
 import org.apache.spark.mllib.tree.model.{GradientBoostedTreesModel => OldGBTModel}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types.DoubleType
 
 /**
  * :: Experimental ::
@@ -167,8 +169,15 @@ final class GBTRegressionModel(
 
   override def treeWeights: Array[Double] = _treeWeights
 
+  override protected def transformImpl(dataset: DataFrame): DataFrame = {
+    val bcastModel = dataset.sqlContext.sparkContext.broadcast(this)
+    val predictUDF = udf { (features: Any) =>
+      bcastModel.value.predict(features.asInstanceOf[Vector])
+    }
+    dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
+  }
+
   override protected def predict(features: Vector): Double = {
-    // TODO: Override transform() to broadcast model. SPARK-7127
     // TODO: When we add a generic Boosting class, handle transform there?  SPARK-7129
     // Classifies by thresholding sum of weighted tree predictions
     val treePredictions = _trees.map(_.rootNode.predict(features))
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
index 21c59061a02f..745637a88d6c 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
@@ -29,6 +29,8 @@ import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
 import org.apache.spark.mllib.tree.model.{RandomForestModel => OldRandomForestModel}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types.DoubleType
 
 /**
  * :: Experimental ::
@@ -122,8 +124,15 @@ final class RandomForestRegressionModel private[ml] (
 
   override def treeWeights: Array[Double] = _treeWeights
 
+  override protected def transformImpl(dataset: DataFrame): DataFrame = {
+    val bcastModel = dataset.sqlContext.sparkContext.broadcast(this)
+    val predictUDF = udf { (features: Any) =>
+      bcastModel.value.predict(features.asInstanceOf[Vector])
+    }
+    dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
+  }
+
   override protected def predict(features: Vector): Double = {
-    // TODO: Override transform() to broadcast model.  SPARK-7127
     // TODO: When we add a generic Bagging class, handle transform there.  SPARK-7128
     // Predict average of tree predictions.
     // Ignore the weights since all are 1.0 for now.