Yup, currently PMML export, or Java serialization, are the options realistically available.
Though PMML may deter some, there are not many viable cross-platform alternatives (with nearly as much coverage). On Thu, Nov 12, 2015 at 1:42 PM, Sean Owen <so...@cloudera.com> wrote: > This is all starting to sound a lot like what's already implemented in > Java-based PMML parsing/scoring libraries like JPMML and OpenScoring. I'm > not clear it helps a lot to reimplement this in Spark. > > On Thu, Nov 12, 2015 at 8:05 AM, Felix Cheung <felixcheun...@hotmail.com> > wrote: > >> +1 on that. It would be useful to use the model outside of Spark. >> >> >> _____________________________ >> From: DB Tsai <dbt...@dbtsai.com> >> Sent: Wednesday, November 11, 2015 11:57 PM >> Subject: Re: thought experiment: use spark ML to real time prediction >> To: Nirmal Fernando <nir...@wso2.com> >> Cc: Andy Davidson <a...@santacruzintegration.com>, Adrian Tanase < >> atan...@adobe.com>, user @spark <user@spark.apache.org> >> >> >> >> Do you think it will be useful to separate those models and model >> loader/writer code into another spark-ml-common jar without any spark >> platform dependencies so users can load the models trained by Spark ML in >> their application and run the prediction? >> >> >> Sincerely, >> >> DB Tsai >> ---------------------------------------------------------- >> Web: https://www.dbtsai.com >> PGP Key ID: 0xAF08DF8D >> >> On Wed, Nov 11, 2015 at 3:14 AM, Nirmal Fernando <nir...@wso2.com> >> wrote: >> >>> As of now, we are basically serializing the ML model and then >>> deserialize it for prediction at real time. >>> >>> On Wed, Nov 11, 2015 at 4:39 PM, Adrian Tanase <atan...@adobe.com> >>> wrote: >>> >>>> I don’t think this answers your question but here’s how you would >>>> evaluate the model in realtime in a streaming app >>>> >>>> https://databricks.gitbooks.io/databricks-spark-reference-applications/content/twitter_classifier/predict.html >>>> >>>> Maybe you can find a way to extract portions of MLLib and run them >>>> outside of spark – loading the precomputed model and calling .predict on >>>> it… >>>> >>>> -adrian >>>> >>>> From: Andy Davidson >>>> Date: Tuesday, November 10, 2015 at 11:31 PM >>>> To: "user @spark" >>>> Subject: thought experiment: use spark ML to real time prediction >>>> >>>> Lets say I have use spark ML to train a linear model. I know I can save >>>> and load the model to disk. I am not sure how I can use the model in a real >>>> time environment. For example I do not think I can return a “prediction” to >>>> the client using spark streaming easily. Also for some applications the >>>> extra latency created by the batch process might not be acceptable. >>>> >>>> If I was not using spark I would re-implement the model I trained in my >>>> batch environment in a lang like Java and implement a rest service that >>>> uses the model to create a prediction and return the prediction to the >>>> client. Many models make predictions using linear algebra. Implementing >>>> predictions is relatively easy if you have a good vectorized LA package. Is >>>> there a way to use a model I trained using spark ML outside of spark? >>>> >>>> As a motivating example, even if its possible to return data to the >>>> client using spark streaming. I think the mini batch latency would not be >>>> acceptable for a high frequency stock trading system. >>>> >>>> Kind regards >>>> >>>> Andy >>>> >>>> P.s. The examples I have seen so far use spark streaming to >>>> “preprocess” predictions. For example a recommender system might use what >>>> current users are watching to calculate “trending recommendations”. These >>>> are stored on disk and served up to users when the use the “movie guide”. >>>> If a recommendation was a couple of min. old it would not effect the end >>>> users experience. >>>> >>>> >>> >>> >>> -- >>> >>> Thanks & regards, >>> Nirmal >>> >>> Team Lead - WSO2 Machine Learner >>> Associate Technical Lead - Data Technologies Team, WSO2 Inc. >>> Mobile: +94715779733 >>> Blog: http://nirmalfdo.blogspot.com/ >>> >>> >>> >> >> >> >
