diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
index 9aaafa34f8c03..5070032e49809 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
@@ -27,11 +27,9 @@ import breeze.linalg.{DenseVector => BDV, SparseVector => BSV, Vector => BV}
 
 import org.apache.spark.SparkException
 import org.apache.spark.mllib.util.NumericParser
-import org.apache.spark.sql.catalyst.UDTRegistry
 import org.apache.spark.sql.catalyst.annotation.SQLUserDefinedType
 import org.apache.spark.sql.catalyst.expressions.GenericMutableRow
 import org.apache.spark.sql.catalyst.types._
-import org.apache.spark.sql.Row
 
 /**
  * Represents a numeric vector, whose index type is Int and value type is Double.
@@ -86,12 +84,6 @@ sealed trait Vector extends Serializable {
  */
 object Vectors {
 
-  // Note: Explicit registration is only needed for Vector and SparseVector;
-  //       the annotation works for DenseVector.
-  UDTRegistry.registerType(scala.reflect.runtime.universe.typeOf[Vector], new VectorUDT())
-  UDTRegistry.registerType(scala.reflect.runtime.universe.typeOf[SparseVector],
-    new SparseVectorUDT())
-
   /**
    * Creates a dense vector from its values.
    */
@@ -202,7 +194,6 @@ object Vectors {
 /**
  * A dense vector represented by a value array.
  */
-@SQLUserDefinedType(udt = classOf[DenseVectorUDT])
 class DenseVector(val values: Array[Double]) extends Vector {
 
   override def size: Int = values.length
@@ -254,105 +245,3 @@ class SparseVector(
 
   private[mllib] override def toBreeze: BV[Double] = new BSV[Double](indices, values, size)
 }
-
-/**
- * User-defined type for [[Vector]] which allows easy interaction with SQL
- * via [[org.apache.spark.sql.SchemaRDD]].
- */
-private[spark] class VectorUDT extends UserDefinedType[Vector] {
-
-  /**
-   * vectorType: 0 = dense, 1 = sparse.
-   * dense, sparse: One element holds the vector, and the other is null.
-   */
-  override def sqlType: StructType = StructType(Seq(
-    StructField("vectorType", ByteType, nullable = false),
-    StructField("dense", new DenseVectorUDT, nullable = true),
-    StructField("sparse", new SparseVectorUDT, nullable = true)))
-
-  override def serialize(obj: Any): Row = {
-    val row = new GenericMutableRow(3)
-    obj match {
-      case v: DenseVector =>
-        row.setByte(0, 0)
-        row.update(1, new DenseVectorUDT().serialize(obj))
-        row.setNullAt(2)
-      case v: SparseVector =>
-        row.setByte(0, 1)
-        row.setNullAt(1)
-        row.update(2, new SparseVectorUDT().serialize(obj))
-    }
-    row
-  }
-
-  override def deserialize(datum: Any): Vector = {
-    datum match {
-      case row: Row =>
-        require(row.length == 3,
-          s"VectorUDT.deserialize given row with length ${row.length} but requires length == 3")
-        val vectorType = row.getByte(0)
-        vectorType match {
-          case 0 =>
-            new DenseVectorUDT().deserialize(row.getAs[Row](1))
-          case 1 =>
-            new SparseVectorUDT().deserialize(row.getAs[Row](2))
-        }
-    }
-  }
-}
-
-/**
- * User-defined type for [[DenseVector]] which allows easy interaction with SQL
- * via [[org.apache.spark.sql.SchemaRDD]].
- */
-private[spark] class DenseVectorUDT extends UserDefinedType[DenseVector] {
-
-  override def sqlType: DataType = ArrayType(DoubleType, containsNull = false)
-
-  override def serialize(obj: Any): Seq[Double] = {
-    obj match {
-      case v: DenseVector =>
-        v.values.toSeq
-    }
-  }
-
-  override def deserialize(datum: Any): DenseVector = {
-    datum match {
-      case values: Seq[_] =>
-        new DenseVector(values.asInstanceOf[Seq[Double]].toArray)
-    }
-  }
-}
-
-/**
- * User-defined type for [[SparseVector]] which allows easy interaction with SQL
- * via [[org.apache.spark.sql.SchemaRDD]].
- */
-private[spark] class SparseVectorUDT extends UserDefinedType[SparseVector] {
-
-  override def sqlType: StructType = StructType(Seq(
-    StructField("size", IntegerType, nullable = false),
-    StructField("indices", ArrayType(IntegerType, containsNull = false), nullable = false),
-    StructField("values", ArrayType(DoubleType, containsNull = false), nullable = false)))
-
-  override def serialize(obj: Any): Row = obj match {
-    case v: SparseVector =>
-      val row: GenericMutableRow = new GenericMutableRow(3)
-      row.setInt(0, v.size)
-      row.update(1, v.indices.toSeq)
-      row.update(2, v.values.toSeq)
-      row
-  }
-
-  override def deserialize(datum: Any): SparseVector = {
-    datum match {
-      case row: Row =>
-        require(row.length == 3,
-          s"SparseVectorUDT.deserialize given row with length ${row.length} but expect 3.")
-        val vSize = row.getInt(0)
-        val indices = row.getAs[Seq[Int]](1).toArray
-        val values = row.getAs[Seq[Double]](2).toArray
-        new SparseVector(vSize, indices, values)
-    }
-  }
-}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/dataTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/dataTypes.scala
index 4f0ac6ebbb604..df35577b659c3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/dataTypes.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/types/dataTypes.scala
@@ -30,7 +30,7 @@ import org.json4s.JsonDSL._
 import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.sql.catalyst.{UDTRegistry, ScalaReflectionLock}
+import org.apache.spark.sql.catalyst.ScalaReflectionLock
 import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference, Expression, Row}
 import org.apache.spark.sql.catalyst.types.decimal._
 import org.apache.spark.sql.catalyst.util.Metadata