Let's suppose you have trained a LogisticRegressionModel and saved it at "/tmp/lr-model". You can copy the directory to production environment and use it to make prediction on users new data. You can refer the following code snippets:
val model = LogisiticRegressionModel.load("/tmp/lr-model") val data = newDataset val prediction = model.transform(data) However, usually we save/load PipelineModel which include necessary feature transformers and model training process rather than the single model, but they are similar operations. Thanks Yanbo 2016-06-23 10:54 GMT-07:00 Saurabh Sardeshpande <saurabh...@gmail.com>: > Hi all, > > How do you reliably deploy a spark model in production? Let's say I've > done a lot of analysis and come up with a model that performs great. I have > this "model file" and I'm not sure what to do with it. I want to build some > kind of service around it that takes some inputs, converts them into a > feature, runs the equivalent of 'transform', i.e. predict the output and > return the output. > > At the Spark Summit I heard a lot of talk about how this will be easy to > do in Spark 2.0, but I'm looking for an solution sooner. PMML support is > limited and the model I have can't be exported in that format. > > I would appreciate any ideas around this, especially from personal > experiences. > > Regards, > Saurabh >