Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/20592#discussion_r167847513
--- Diff: docs/ml-pipeline.md ---
@@ -188,9 +188,36 @@ Parameters belong to specific instances of
`Estimator`s and `Transformer`s.
For example, if we have two `LogisticRegression` instances `lr1` and
`lr2`, then we can build a `ParamMap` with both `maxIter` parameters specified:
`ParamMap(lr1.maxIter -> 10, lr2.maxIter -> 20)`.
This is useful if there are two algorithms with the `maxIter` parameter in
a `Pipeline`.
-## Saving and Loading Pipelines
+## ML persistence: Saving and Loading Pipelines
-Often times it is worth it to save a model or a pipeline to disk for later
use. In Spark 1.6, a model import/export functionality was added to the
Pipeline API. Most basic transformers are supported as well as some of the more
basic ML models. Please refer to the algorithm's API documentation to see if
saving and loading is supported.
+Often times it is worth it to save a model or a pipeline to disk for later
use. In Spark 1.6, a model import/export functionality was added to the
Pipeline API.
+As of Spark 2.3, the DataFrame-based API in `spark.ml` and `pyspark.ml`
has complete coverage.
+
+ML persistence works across Scala, Java and Python. However, R currently
uses a modified format,
+so models saved in R can only be loaded back in R; this should be fixed in
the future and is
+tracked in
[SPARK-15572](https://issues.apache.org/jira/browse/SPARK-15572).
+
+### Backwards compatibility for ML persistence
+
+In general, MLlib maintains backwards compatibility for ML persistence.
I.e., if you save an ML
--- End diff --
Oh, I was in this too before. IIRC, I was confused of an RDD vs a RDD. I
learnt English needs "an" vs "a" is by how it pronounce. I believe "an em-el"
is correct :).
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