Exploiting KSQL stream transformation and user-defined functions to deploy realtime machine learning models

Christopher Harris
Apr 16, 2019 · 5 min read
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Setting up KSQL

Training the Model

Implementing the Model in KSQL

java -jar files/jars/jpmml-sklearn-executable-1.5-SNAPSHOT.jar --pkl-input ./models/iris-pipeline.pkl.z --pmml-output ./models/iris-pipeline.pmml
java -cp target/ksql-ml-pmml-example-0.0.1-jar-with-dependencies.jar ksqlexample.pmml.LocalRun models/iris-pipeline.pmml 7.0,3.2,4.7,4.4
cp models/iris-pipeline.pmml docker/volume/
cp target/ksql-ml-pmml-example-0.0.1-jar-with-dependencies.jar docker/volume/

Realtime Model Evaluation Demo

docker-compose exec broker kafka-console-producer  --broker-list broker:29092 --topic iris_data
>{"sepal_length": 5.1,"sepal_width": 3.5,"petal_length": 1.4,"petal_width": 0.2}
>{"sepal_length": 5.9,"sepal_width": 3.0,"petal_length": 5.1,"petal_width": 1.8}
>{"sepal_length": 6.3,"sepal_width": 2.5,"petal_length": 4.9,"petal_width": 1.5}
CREATE STREAM iris_filtered AS SELECT * FROM iris_stream WHERE sepal_width > 3.0;
CREATE STREAM iris_to_inches AS SELECT sepal_length/2.54, sepal_width/2.54, petal_length/2.54, petal_width/2.54 FROM iris_stream;


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