2. #RstatsNYC @Socure
• Real-time fraud detection service using social and online data.
• Predictive R models.
• Latency SLA with customers.
• Model versioning.
• Zero-downtime updates.
3. #RstatsNYC @Socure
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.
4. #RstatsNYC @Socure
Solution
• Embed R models within dev-op friendly middleware.
• Management, deployment, integration leverages existing dev-op
processes.
• Service scaling using established strategies and methods.
17. #RstatsNYC @Socure
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.