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- SPARK-45397
Add vector assembler feature transformer - SPARK-45396
Add doc entry for `pyspark.ml.connect` module - SPARK-45130
Avoid Spark connect ML model to change input pandas dataframe - SPARK-45129
Add pyspark "ml-connect" extras dependencies - SPARK-44374
Add example code - SPARK-44250
Implement classification evaluator - SPARK-44100
Move namespace from `pyspark.mlv2` to `pyspark.ml.connect` - SPARK-43983
Implement cross validator estimator - SPARK-43982
Implement pipeline estimator - SPARK-43981
Basic saving / loading implementation - SPARK-43790
Add API `copyLocalFileToHadoopFS` - SPARK-43715
Add spark DataFrame binary file format writer - SPARK-43516
Basic estimator / transformer / model / evaluator interfaces and basic transformer / evaluator implementation - SPARK-43097
Implement pyspark ML logistic regression estimator on top of torch distributor - SPARK-43081
Add torch distributor data loader that loads data from spark partition data - SPARK-42994
Torch Distributor support Local Mode - SPARK-42993
Make Torch Distributor compatible with Spark Connect - SPARK-42870
Move `toCatalystValue` to connect-common - SPARK-42800
Implement ml function {array_to_vector, vector_to_array} - SPARK-42756
Helper function to convert proto literal to value in Python Client - SPARK-42755
Factor literal value conversion out to connect-common - SPARK-42725
Make LiteralExpression support array - SPARK-42508
Extract the common .ml classes to `mllib-common` - SPARK-42501
High level design doc for Distributed ML <> spark connect - SPARK-42472
Make spark connect supporting canceling job group - SPARK-42412
Initial prototype implementation for PySparkML
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