Github user viirya commented on a diff in the pull request:
    --- Diff: mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala 
    @@ -290,6 +293,27 @@ object Bucketizer extends 
DefaultParamsReadable[Bucketizer] {
    +  private[Bucketizer] class BucketizerWriter(instance: Bucketizer) extends 
MLWriter {
    +    override protected def saveImpl(path: String): Unit = {
    +      // SPARK-23377: The default params will be saved and loaded as 
user-supplied params.
    +      // Once `inputCols` is set, the default value of `outputCol` param 
causes the error
    +      // when checking exclusive params. As a temporary to fix it, we 
remove the default
    +      // value of `outputCol` if `inputCols` is set before saving.
    +      // TODO: If we modify the persistence mechanism later to better 
handle default params,
    +      // we can get rid of this.
    +      var removedOutputCol: Option[String] = None
    --- End diff --
    I was thinking about this too. But looks like we don't add lock to the 
places we change params in ML. I guess that we assume the usage of ML models is 
single-threaded. So I leave it as this. Will add it if others think this is 
required too.


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