diff --git a/mllib/src/main/scala/org/apache/spark/ml/clustering/LDA.scala b/mllib/src/main/scala/org/apache/spark/ml/clustering/LDA.scala
index fe6a37fd6dc3..4fc5a81b985a 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/clustering/LDA.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/clustering/LDA.scala
@@ -18,7 +18,10 @@
 package org.apache.spark.ml.clustering
 
 import org.apache.hadoop.fs.Path
+import org.json4s.DefaultFormats
+import org.json4s.jackson.JsonMethods._
 
+import org.apache.spark.SparkException
 import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.internal.Logging
 import org.apache.spark.ml.{Estimator, Model}
@@ -457,6 +460,31 @@ sealed abstract class LDAModel private[ml] (
   def describeTopics(): DataFrame = describeTopics(10)
 }
 
+object LDAModel extends MLReadable[LDAModel] {
+
+  private class LDAModelReader extends MLReader[LDAModel] {
+    override def load(path: String): LDAModel = {
+      val metadataPath = new Path(path, "metadata").toString
+      val metadata = parse(sc.textFile(metadataPath, 1).first())
+      implicit val format = DefaultFormats
+      val className = (metadata \ "class").extract[String]
+      className match {
+        case c if className == classOf[LocalLDAModel].getName =>
+          LocalLDAModel.load(path)
+        case c if className == classOf[DistributedLDAModel].getName =>
+          DistributedLDAModel.load(path)
+        case _ => throw new SparkException(s"$className in $path is not a LDAModel")
+      }
+    }
+  }
+
+  @Since("2.0.0")
+  override def read: MLReader[LDAModel] = new LDAModelReader
+
+  @Since("2.0.0")
+  override def load(path: String): LDAModel = super.load(path)
+}
+
 
 /**
  * :: Experimental ::
diff --git a/mllib/src/test/scala/org/apache/spark/ml/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/ml/clustering/LDASuite.scala
index dd3f4c6e5391..4805995c1ffa 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/clustering/LDASuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/clustering/LDASuite.scala
@@ -17,12 +17,15 @@
 
 package org.apache.spark.ml.clustering
 
+import java.io.File
+
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.mllib.util.TestingUtils._
 import org.apache.spark.sql.{DataFrame, Row, SQLContext}
+import org.apache.spark.util.Utils
 
 
 object LDASuite {
@@ -261,4 +264,22 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext with DefaultRead
     testEstimatorAndModelReadWrite(lda, dataset,
       LDASuite.allParamSettings ++ Map("optimizer" -> "em"), checkModelData)
   }
+
+  test("load LDAModel") {
+    val lda = new LDA().setK(k).setSeed(1).setOptimizer("em").setMaxIter(2)
+    val distributedModel = lda.fit(dataset)
+    val localModel = lda.setOptimizer("online").fit(dataset)
+
+    val tempDir1 = Utils.createTempDir()
+    val distributedPath = new File(tempDir1, "distributed").getPath
+    val localPath = new File(tempDir1, "local").getPath
+    try {
+      distributedModel.save(distributedPath)
+      localModel.save(localPath)
+      assert(LDAModel.load(distributedPath).isInstanceOf[DistributedLDAModel])
+      assert(LDAModel.load(localPath).isInstanceOf[LocalLDAModel])
+    } finally {
+      Utils.deleteRecursively(tempDir1)
+    }
+  }
 }