How can I operationalize a SparkML model as a real-time webservice?

Question

Once a SparkML model has been trained on a Spark cluster, how can I take the trained model and make it available for scoring through a restful API?

The problem is that it requires a SparkContext in order to be loaded, but is there a way to 'fake it' since it does not seem really necessary, or what is the minimum required to create a SparkContext?

You can take a look at this previous question: https://stackoverflow.com/questions/40533582/how-to-serve-a-spark-mllib-model — Shaido, Aug 16 '17 at 09:48

score 1 · Accepted Answer · answered Aug 16 '17 at 09:41

In some cases - yes, it can.

Many models in Spark can be exported to JPMML, standarized format for ML models. Then you can use it with other Java library like https://github.com/jpmml/jpmml-sparkml

How to export you can read in this question - Spark ml and PMML export.

You can also use Spark Streaming to calculate values, however it will have higher latency until Continous Processing Mode being available

For very time-consuming calculations, such as recommendation algorithms, it's I think quite normal to pre-calculate values and save in database like Cassandra

How can I operationalize a SparkML model as a real-time webservice?

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