Hello, I trained a linear regression model with spark-ml. I serialized the model pipeline with classical java serialization. Then I loaded it in a webservice to compute predictions.
For each request recieved by the webservice I create a 1 row dataframe to compute that prediction. Probleme is that it take too much time.... Is there some good practices to do that kind of stuff ? I could export all model's coeffs with PMML and make computations in pure java but I keep it in last resort. Does any one have some hints to increase performances ? Philippe -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Performance-issue-with-spark-ml-model-to-make-single-predictions-on-server-side-tp27217.html Sent from the Apache Spark User List mailing list archive at Nabble.com. --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
