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.
