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/
