The document discusses scalable prediction services for real-time fraud detection using R models, focusing on challenges like the lack of DevOps compatibility and limited model types. It proposes solutions such as embedding R models within middleware to enhance management and deployment processes while leveraging existing DevOps strategies. The conclusions highlight the rapid deployment of R models in scalable environments, effective integration with data science workflows, and potential future serialization of models for compliance.

1. Scalable Prediction Services with R

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• Real-time fraud detection service using social and online data.
• Predictive R models.
• Latency SLA with customers.
• Model versioning.
• Zero-downtime updates.

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Challenges
• R not dev-ops friendly.
• Enterprise prediction services a large commitment.
• Enterprise prediction services offer limited model types.
• Transferability and transparency of models.
• Vendor lock-in.

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Solution
• Embed R models within dev-op friendly middleware.
• Management, deployment, integration leverages existing dev-op
processes.
• Service scaling using established strategies and methods.

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<file>
gen_20150215.rds
saveRDS()

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<model>
name = generic
version = 20150215
<file>
gen_20150215.rds
readRDS()
saveRDS()

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Rook
http://…./model/20150215
<model>
name = generic
version = 20150215
name version
model 20150215
Model Map
<file>
gen_20150215.rds
readRDS()
saveRDS()

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Rook
http://…./model/20150215
<model>
name = generic
version = 20150215
name version
model 20150215
Model Map
predict()
<file>
gen_20150215.rds
readRDS()
saveRDS()
JSON

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POST generic/20150215
Rook Rook Rook Rook
fork()
……..

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pmml
http://…./generic/20150215
org.jpmml.evaluator
ModelEvaluatordoPost()
Servlet
evaluate()
unmarshalPMML()
pmml.gbm()

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ServletServletServletServlet
POST generic/20150215
……..

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Virtual Machine
Docker Public Repository ECS
ElasticBeanStalk
R R RR R R

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http://…./generic/20150215
ElasticBeanStalk
Prediction
Service
Prediction
ServicePrediction
Service
US-EAST-1A
Prediction
Service
Prediction
ServicePrediction
Service
US-EAST-1A
Prediction
Service
Prediction
ServicePrediction
Service
US-EAST-1A

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Conclusions
• Rapid deployment of R models in a scalable robust environment.
• Directly leverage R models developed by data scientists and
analysts.
• Apply existing dev-ops processes for testing, monitoring, scaling,
alerting of predictive models.
• Possible use of PMML to serialize models in future for compliance.

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GitHub
https://github.com/Socure/moduleR

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