Online predictive analytics

JPMML prediction processor evaluates event feature vectors in near-real-time

Objective

Provide streaming predictions and scoring using a standard PMML online predictor implementation.

The implementation leverages the JPMML open source library developed by Villu Ruusmann

Example & DSL attributes

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The example below demonstrates the ease at which a machine learning model can be embedded within a stream processing pipeline and have events scored.

Model preparation

On Joule startup the following is performed before events are processed:

  1. Model is loaded in to memory from disk.

  2. The required feature vector attributes are derived from the model ready to be retrieved from the presented event.

  3. A response field is configured.

Example

pmml predictor:
  name: irisScorer
  model: ./models/iris_rf.pmml
  response field: flowerPrediction

Model scoring

Events are scored in near real-time using this process:

  1. Extract the required feature vector attributes from the event.

  2. Execute the model inferencing function using the prepared feature vector.

  3. Place the resulting score in to the event assigned to the response field.

Attributes schema

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