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:
Model is loaded in to memory from disk.
The required feature vector attributes are derived from the model ready to be retrieved from the presented event.
A response field is configured.
Example
Model scoring
Events are scored in near real-time using this process:
Extract the required feature vector attributes from the event.
Execute the model inferencing function using the prepared feature vector.
Place the resulting score in to the event assigned to the
response field
.
Attributes schema
name
name of the counter.
String
model
model filename that specifies path and model filename.
String
model store
Reference data logical store name where the model resides. See Contextual data documentation for further information on configuration
String
features field
Field where all the required features exist for the model prediction function. This is an optional field where engineered map of features are placed and applied to the model evaluation. See the Feature Engineering documentation for further details
String
response field
Name of field where the result of the prediction function will be assigned too
String
unpack results
Unpack the algorithm response variables directly into the StreamEvent
object. Otherwise the process will add the complete response object in to the response field
Boolean
Default: false
audit configuration
Every prediction can be audited along with the features used.
See model audit page
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