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/ >> >> >> > > >
