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 3073a2a61ce83..1bd86fdb28f49 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
@@ -21,6 +21,7 @@ import com.github.fommil.netlib.BLAS.{getInstance => blas}
 
 import org.apache.spark.Logging
 import org.apache.spark.annotation.Experimental
+import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.ml.{PredictionModel, Predictor}
 import org.apache.spark.ml.param.{Param, ParamMap}
 import org.apache.spark.ml.regression.DecisionTreeRegressionModel
@@ -175,14 +176,20 @@ final class GBTClassificationModel(
   require(_trees.length == _treeWeights.length, "GBTClassificationModel given trees, treeWeights" +
     s" of non-matching lengths (${_trees.length}, ${_treeWeights.length}, respectively).")
 
+  private var bcastModel: Option[Broadcast[GBTClassificationModel]] = None
+
   override def trees: Array[DecisionTreeModel] = _trees.asInstanceOf[Array[DecisionTreeModel]]
 
   override def treeWeights: Array[Double] = _treeWeights
 
   override protected def transformImpl(dataset: DataFrame): DataFrame = {
-    val bcastModel = dataset.sqlContext.sparkContext.broadcast(this)
+    bcastModel match {
+      case None => bcastModel = Some(dataset.sqlContext.sparkContext.broadcast(this))
+      case _ =>
+    }
+    val lclBcastModel = bcastModel
     val predictUDF = udf { (features: Any) =>
-      bcastModel.value.predict(features.asInstanceOf[Vector])
+      lclBcastModel.get.value.predict(features.asInstanceOf[Vector])
     }
     dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
   }
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 11a6d72468333..3e3d8378bfe7c 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
@@ -18,6 +18,7 @@
 package org.apache.spark.ml.classification
 
 import org.apache.spark.annotation.Experimental
+import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.ml.tree.impl.RandomForest
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.tree.{DecisionTreeModel, RandomForestParams, TreeClassifierParams, TreeEnsembleModel}
@@ -132,6 +133,8 @@ final class RandomForestClassificationModel private[ml] (
 
   require(numTrees > 0, "RandomForestClassificationModel requires at least 1 tree.")
 
+  private var bcastModel: Option[Broadcast[RandomForestClassificationModel]] = None
+
   /**
    * Construct a random forest classification model, with all trees weighted equally.
    * @param trees  Component trees
@@ -150,9 +153,13 @@ 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)
+    bcastModel match {
+      case None => bcastModel = Some(dataset.sqlContext.sparkContext.broadcast(this))
+      case _ =>
+    }
+    val lclBcastModel = bcastModel
     val predictUDF = udf { (features: Any) =>
-      bcastModel.value.predict(features.asInstanceOf[Vector])
+      lclBcastModel.get.value.predict(features.asInstanceOf[Vector])
     }
     dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
   }
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 b66e61f37dd5e..948dc9b39519c 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
@@ -21,6 +21,7 @@ import com.github.fommil.netlib.BLAS.{getInstance => blas}
 
 import org.apache.spark.Logging
 import org.apache.spark.annotation.Experimental
+import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.ml.{PredictionModel, Predictor}
 import org.apache.spark.ml.param.{Param, ParamMap}
 import org.apache.spark.ml.tree.{DecisionTreeModel, GBTParams, TreeEnsembleModel, TreeRegressorParams}
@@ -165,14 +166,20 @@ final class GBTRegressionModel(
   require(_trees.length == _treeWeights.length, "GBTRegressionModel given trees, treeWeights of" +
     s" non-matching lengths (${_trees.length}, ${_treeWeights.length}, respectively).")
 
+  private var bcastModel: Option[Broadcast[GBTRegressionModel]] = None
+
   override def trees: Array[DecisionTreeModel] = _trees.asInstanceOf[Array[DecisionTreeModel]]
 
   override def treeWeights: Array[Double] = _treeWeights
 
   override protected def transformImpl(dataset: DataFrame): DataFrame = {
-    val bcastModel = dataset.sqlContext.sparkContext.broadcast(this)
+    bcastModel match {
+      case None => bcastModel = Some(dataset.sqlContext.sparkContext.broadcast(this))
+      case _ =>
+    }
+    val lclBcastModel = bcastModel
     val predictUDF = udf { (features: Any) =>
-      bcastModel.value.predict(features.asInstanceOf[Vector])
+      lclBcastModel.get.value.predict(features.asInstanceOf[Vector])
     }
     dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
   }
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 2f36da371f577..fb6235526db7d 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
@@ -18,6 +18,7 @@
 package org.apache.spark.ml.regression
 
 import org.apache.spark.annotation.Experimental
+import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.ml.{PredictionModel, Predictor}
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.tree.{DecisionTreeModel, RandomForestParams, TreeEnsembleModel, TreeRegressorParams}
@@ -121,6 +122,8 @@ final class RandomForestRegressionModel private[ml] (
 
   require(numTrees > 0, "RandomForestRegressionModel requires at least 1 tree.")
 
+  private var bcastModel: Option[Broadcast[RandomForestRegressionModel]] = None
+
   /**
    * Construct a random forest regression model, with all trees weighted equally.
    * @param trees  Component trees
@@ -136,9 +139,13 @@ 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)
+    bcastModel match {
+      case None => bcastModel = Some(dataset.sqlContext.sparkContext.broadcast(this))
+      case _ =>
+    }
+    val lclBcastModel = bcastModel
     val predictUDF = udf { (features: Any) =>
-      bcastModel.value.predict(features.asInstanceOf[Vector])
+      lclBcastModel.get.value.predict(features.asInstanceOf[Vector])
     }
     dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
   }