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Building Real Time Targeting Capabilities - Ryan Zotti, Subbu Thiruppathy - Capital One

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A team of data and software engineers and data scientists at Capital One are experimenting with various technologies to enable lightning-fast promotional content that visitors will see when they visit Capital One’s website looking to apply for a credit card. In this presentation we’ll first talk about some of the technologies that we’re exploring such as the Akka-based Play framework, and H2O, a popular open source machine learning library. We will explore our evolution of data science and the H2O tools used to create the groundwork for continuous and automated testing and optimization, with the ability to scale across the entire company. Then conclude with a quick demo followed by a few tips and tricks that we learned along the way. #h2ony

- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata

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Building Real Time Targeting Capabilities - Ryan Zotti, Subbu Thiruppathy - Capital One

  1. 1. Building Real Time Targeting Capabilities Capital One | Fast Marketing July 20, 2016 | H20 Open Tour | NYC
  2. 2. Ryan Zotti Senior Data Engineer Subbu Thiruppathy Senior Software Engineer EXPERTISE FUN FACT Big Data, Python, R, Java, Machine Learning, AWS FUN FACT Big Data, Java, AKKA Play, AWS Built a self driving remote controlled car Recipient of Capital One’s most prestigious honor EXPERTISE
  3. 3. http://www.tibco.com/blog/2015/05/26/upcoming-webinar-integration-as-the-foundation-of-fast-data-may-28-2/
  4. 4. Source: http://thumbs.dreamstime.com/x/65-miles-per-hour-7772157.jpg https://morganalyx.wordpress.com/2013/02/22/assertive-driving/
  5. 5. http://www.dailymail.co.uk/health/article-2467478/What-causes-dry-eye-syndrome-cure-treatment.html
  6. 6. MODEL DATA MODEL DEPLOYMENT MODEL SCORING MODEL TRAINING Our challenge is… …striving to be fast at everything
  7. 7. Most current, up-to-date data Available as soon as it’s ready Low latency at scale FAST MODEL DATA
  8. 8. Most current, up-to-date data Available as soon as it’s ready Low latency at scale FAST MODEL DATA
  9. 9. Most current, up-to-date data Available as soon as it’s ready Low latency at scale FAST MODEL DATA
  10. 10. Most current, up-to-date data Available as soon as it’s ready Low latency at scale FAST MODEL DATA
  11. 11. Distributed computing to crunch data fast Elastic scaling with the public cloud Speed from parallelism FAST MODEL TRAINING
  12. 12. Distributed computing to crunch data fast Elastic scaling with the public cloud Speed from parallelism FAST MODEL TRAINING
  13. 13. Distributed computing to crunch data fast Elastic scaling with the public cloud Speed from parallelism FAST MODEL TRAINING
  14. 14. Distributed computing to crunch data fast Elastic scaling with the public cloud Speed from parallelism FAST MODEL TRAINING
  15. 15. Model adapts to evolving customer landscape Automatically refit the model and daily deploy Seamlessly integrate with existing Java tech stack FAST MODEL DEPLOYMENT
  16. 16. Model adapts to evolving customer landscape Automatically refit the model and daily deploy Seamlessly integrate with existing Java tech stack FAST MODEL DEPLOYMENT
  17. 17. Model adapts to evolving customer landscape Automatically refit the model and daily deploy Seamlessly integrate with existing Java tech stack FAST MODEL DEPLOYMENT
  18. 18. Model adapts to evolving customer landscape Automatically refit the model and daily deploy Seamlessly integrate with existing Java tech stack FAST MODEL DEPLOYMENT
  19. 19. Response < 100 milliseconds JVM-based model (i.e. POJO) Predictive power vs. runtime complexity (speed) Gradient boosting provided the best balance FAST MODEL SCORING
  20. 20. Response < 100 milliseconds JVM-based model (i.e. POJO) Predictive power vs. runtime complexity (speed) Gradient boosting provided the best balance FAST MODEL SCORING
  21. 21. Response < 100 milliseconds JVM-based model (i.e. POJO) Predictive power vs. runtime complexity (speed) Gradient boosting provided the best balance FAST MODEL SCORING
  22. 22. Response < 100 milliseconds JVM-based model (i.e. POJO) Predictive power vs. runtime complexity (speed) Gradient boosting provided the best balance FAST MODEL SCORING
  23. 23. Response < 100 milliseconds JVM-based model (i.e. POJO) Predictive power vs. runtime complexity (speed) Gradient boosting provided the best balance FAST MODEL SCORING
  24. 24. VISITOR WEBSITE API MODEL DATA
  25. 25. Explore new technologies continuously Ability to switch new models “on-the-fly” Make the API faster Incorporate new data sources Resiliency, failover capabilities
  26. 26. Technology changes Flexibility of the cloud Keep it simple Small empowered teams
  27. 27. THANK YOU

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