About deployment/serving SPIP https://issues.apache.org/jira/browse/SPARK-26247
________________________________ From: Riccardo Ferrari <ferra...@gmail.com> Sent: Tuesday, January 22, 2019 8:07 AM To: User Subject: I have trained a ML model, now what? Hi list! I am writing here to here about your experience on putting Spark ML models into production at scale. I know it is a very broad topic with many different faces depending on the use-case, requirements, user base and whatever is involved in the task. Still I'd like to open a thread about this topic that is as important as properly training a model and I feel is often neglected. The task is serving web users with predictions and the main challenge I see is making it agile and swift. I think there are mainly 3 general categories of such deployment that can be described as: * Offline/Batch: Load a model, performs the inference, store the results in some datasotre (DB, indexes,...) * Spark in the loop: Having a long running Spark context exposed in some way, this include streaming as well as some custom application that wraps the context. * Use a different technology to load the Spark MLlib model and run the inference pipeline. I have read about MLeap and other PMML based solutions. I would love to hear about opensource solutions and possibly without requiring cloud provider specific framework/component. Again I am aware each of the previous category have benefits and drawback, so what would you pick? Why? and how? Thanks!
