Hollin Wilkins created SPARK-9084:
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Summary: Add in support for realtime data predictions using ML
PipelineModel
Key: SPARK-9084
URL: https://issues.apache.org/jira/browse/SPARK-9084
Project: Spark
Issue Type: New Feature
Components: Spark Core
Reporter: Hollin Wilkins
Priority: Critical
Currently ML provides excellent support for feature manipulation, model
selection, and prediction for large datasets. The models can all be easily
serialized but currently it is not possible to use the fitted models without a
DataFrame. This means that these models are only good for batch processing. In
order to support realtime ML pipelines, I propose adding in three new methods
to the Transformer class:
def transform(row: StructuredRow): StructuredRow
def transform(row: StructuredRow, paramMap: ParamMap): StructuredRow
def transform(row: StructuredRow, firstParamPair: ParamPair[_],
otherParamPairs: ParamPair[_]*): StructuredRow
Where a StructuredRow is a case class that is the combination of an
org.apache.spark.sql.Row and an org.apache.spark.sql.types.StructType
This change necessitates the addition of the new transform method to each
implementor of the Transformer class.
Following this change, it would be trivial to include the spark jars in an API
server, deserialize an ML PipelineModel object, take incoming data from users,
convert it into a StructuredRow and feed it into the PipelineModel to get a
realtime result.
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