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https://issues.apache.org/jira/browse/SPARK-8546?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15327167#comment-15327167
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Radoslaw Gasiorek edited comment on SPARK-8546 at 6/13/16 12:43 PM:
hi there, [~josephkb], [~apachespark]
We would like to use Mllib built models to classify outside spark therefore
without Spark context available. We would like to export the models built in
spark into PMML format, that then would be read by a stand alone java
application without spark context (but with Mllib jar).
The java application would load the model from the PMML file and would use the
model to 'predict' or rather 'classify' the new data we get.
This feature would enable us to proceed without big architectural and
operational changes, without this feature we might need get the the
sparkContext available to the standalone application that would be bigger
operational and architectural overhead.
We might need to use the plain java serialization for the proof of concept
anyways, but surely not for produtionized product.
Can we prioritize this feature as well as
https://issues.apache.org/jira/browse/SPARK-8542 and
https://issues.apache.org/jira/browse/SPARK-8543 ?
What would be LOE and EAT for these?
thanks guys in advance for responses, and feedback.
was (Author: rgasiorek):
hi there, [~josephkb]
We would like to use Mllib built models to classify outside spark therefore
without Spark context available. We would like to export the models built in
spark into PMML format, that then would be read by a stand alone java
application without spark context (but with Mllib jar).
The java application would load the model from the PMML file and would use the
model to 'predict' or rather 'classify' the new data we get.
This feature would enable us to proceed without big architectural and
operational changes, without this feature we might need get the the
sparkContext available to the standalone application that would be bigger
operational and architectural overhead.
We might need to use the plain java serialization for the proof of concept
anyways, but surely not for produtionized product.
Can we prioritize this feature as well as
https://issues.apache.org/jira/browse/SPARK-8542 and
https://issues.apache.org/jira/browse/SPARK-8543 ?
What would be LOE and EAT for these?
thanks guys in advance for responses, and feedback.
> PMML export for Naive Bayes
> ---
>
> Key: SPARK-8546
> URL: https://issues.apache.org/jira/browse/SPARK-8546
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
>Reporter: Joseph K. Bradley
>Assignee: Xusen Yin
>Priority: Minor
>
> The naive Bayes section of PMML standard can be found at
> http://www.dmg.org/v4-1/NaiveBayes.html. We should first figure out how to
> generate PMML for both binomial and multinomial naive Bayes models using
> JPMML (maybe [~vfed] can help).
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