Production Grade Data Science for Hadoop

1. Production Grade
Data Science for Hadoop
Villu Ruusmann
Openscoring OÜ

2. About openscoring.io
"Standards-based, Open-source Middleware for
Predictive Analytics Applications"
aka
"Rapid deployment of R and Scikit-Learn models on JVM"
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3. "From Laboratory to Factory"
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4. From grams (to kilograms) to megagrams
Scaling the vessel / Hardware
Scaling the chemical reaction / Software and processes
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5. Scalability through re-engineering
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6. Broader objectives
● Platform
○ Portability of applications
● Application
○ Central governance and dissemination of models
● Model
○ "Decisioning as a Service"
● Decision
○ Traceability, reproducibility, explainability
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7. R and
Scikit-Learn
PMML
PFA
Java and C
Domain-Specific Languages
General-Purpose Languages
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8. The PMML connection
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9. X model producers
Y model consumers
(ten years into past & future)
1 model API
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10. Model API pipeline
Conversion Deployment
Ephermeal:
Persistent, asset-like:
Conversion Maintenance Deployment
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11. Conversion into PMML
Capturing and expressing the essentials of the modeling
workflow using PMML vocabulary:
Input → Feature vector → Response vector → Output
Connecting stable data schemas
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14. Standardized representation
R
ada, cforest, gbm,
randomForest, xgb.Booster
Scikit-Learn
AdaBoostClassifier,
BaggingClassifier,
ExtraTreesClassifier,
GradientBoostingClassifier,
RandomForestClassifier
<MiningModel function="classification">
<Segmentation
multipleModelMethod="weightedAverage"
>
<Segment id="1" weight="1">
<True/>
<TreeModel>
<Node>
...
</Node>
</TreeModel>
</Segment>
...
</Segmentation>
</MiningModel>
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15. Supra-standardized representation
Model model = MiningModelUtil.createClassifierEnsemble(
MultipleModelMethodType.WEIGHTED_AVERAGE,
Arrays.asList(
PMMLUtil.loadModel("xgboost.pmml"),
PMMLUtil.loadModel("keras-mlp.pmml"),
PMMLUtil.loadModel("sklearn-rf.pmml")
),
Arrays.asList(5d/9d, 2d/9d, 2d/9d)
);
PMMLUtil.storeModel(model, "kaggle-submission.pmml");
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16. State machine for model maintenance
Enhanced
Enhanced
Enhanced
StandardizedRaw Enhanced Optimized
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17. Model as a function
Target = f(Active1, Active2, .., Activen)
Outputfeature = ffeature(Target)
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18. Model metadata API
Evaluator evaluator = getEvaluator();
List<FieldName> activeFields = evaluator.getActiveFields();
for(FieldName activeField : activeFields){
DataField dataField = evaluator.getDataField(activeField);
// Inspect data type, operational type, value space etc.
}
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19. Model evaluation API
Evaluator evaluator = getEvaluator();
while(!done){
Map<FieldName, ?> arguments = readInRecord();
Map<FieldName, ?> results = evaluator.evaluate(arguments);
writeOutRecord(results);
}
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20. http://github.com/jpmml/jpmml-${framework}
Volume
Velocity
{ REST }
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21. JPMML-Cascading
Evaluator evaluator = getEvaluator();
PMMLPlanner pmmlPlanner = new PMMLPlanner(evaluator);
pmmlPlanner.setHeadName("input");
pmmlPlanner.setTailName("output");
FlowDef flowDef = ...;
flowDef.addAssemblyPlanner(pmmlPlanner);
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22. JPMML-Pig
grunt> REGISTER jpmml-pig-distributable-1.0.jar;
grunt> DEFINE my_udf org.jpmml.pig.PMMLFunc('model.pmml');
grunt> output = FOREACH input GENERATE my_udf(*);
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23. JPMML-Spark
Evaluator evaluator = getEvaluator();
PMMLFunction pmmlFunction = new PMMLFunction(evaluator);
JavaRDD<Row> input = ...;
JavaRDD<Row> output = input.map(pmmlFunction);
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24. Thoughts on API-driven architecture
● Based on a relevant standard
○ "Conventions over configuration"
● High(est) abstraction level
○ Productivity
○ Maintainability
● End-to-end value proposition
○ Separation of concerns
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25. Q&A
villu@openscoring.io
http://openscoring.io
http://github.com/jpmml
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