Building Real Time Targeting Capabilities Capital One | Fast Marketing

July 20, 2016 | H20 Open Tour | NYC

Ryan ZottiSenior Data Engineer

Subbu ThiruppathySenior Software Engineer

EXPERTISE

FUN FACT

Big Data, Python, R, Java, Machine Learning, AWSFUN FACT

Big Data, Java, AKKA Play, AWS

Built a self driving remote controlled car

Recipient of Capital One’s most prestigious honor

EXPERTISE

http://www.tibco.com/blog/2015/05/26/upcoming-webinar-integration-as-the-foundation-of-fast-data-may-28-2/

Source: http://thumbs.dreamstime.com/x/65-miles-per-hour-7772157.jpghttps://morganalyx.wordpress.com/2013/02/22/assertive-driving/

http://www.dailymail.co.uk/health/article-2467478/What-causes-dry-eye-syndrome-cure-treatment.html

MODEL DATA

MODEL DEPLOYMENT

MODEL SCORING

MODEL TRAINING

Our challenge is…

…striving to be fast at everything

Most current, up-to-date data

Available as soon as it’s ready

Low latency at scale

FAST MODEL DATA

Most current, up-to-date data

Available as soon as it’s ready

Low latency at scale

FAST MODEL DATA

Most current, up-to-date data

Available as soon as it’s ready

Low latency at scale

FAST MODEL DATA

Most current, up-to-date data

Available as soon as it’s ready

Low latency at scale

FAST MODEL DATA

Distributed computing to crunch data fast

Elastic scaling with the public cloud

Speed from parallelism

FAST MODEL TRAINING

Distributed computing to crunch data fast

Elastic scaling with the public cloud

Speed from parallelism

FAST MODEL TRAINING

Distributed computing to crunch data fast

Elastic scaling with the public cloud

Speed from parallelism

FAST MODEL TRAINING

Distributed computing to crunch data fast

Elastic scaling with the public cloud

Speed from parallelism

FAST MODEL TRAINING

Model adapts to evolving customer landscape

Automatically refit the model and daily deploy

Seamlessly integrate with existing Java tech stack

FAST MODEL DEPLOYMENT

Model adapts to evolving customer landscape

Automatically refit the model and daily deploy

Seamlessly integrate with existing Java tech stack

FAST MODEL DEPLOYMENT

Model adapts to evolving customer landscape

Automatically refit the model and daily deploy

Seamlessly integrate with existing Java tech stack

FAST MODEL DEPLOYMENT

Model adapts to evolving customer landscape

Automatically refit the model and daily deploy

Seamlessly integrate with existing Java tech stack

FAST MODEL DEPLOYMENT

Response < 100 milliseconds

JVM-based model (i.e. POJO)

Predictive power vs. runtime complexity (speed)

Gradient boosting provided the best balance

FAST MODEL SCORING

Response < 100 milliseconds

JVM-based model (i.e. POJO)

Predictive power vs. runtime complexity (speed)

Gradient boosting provided the best balance

FAST MODEL SCORING

Response < 100 milliseconds

JVM-based model (i.e. POJO)

Predictive power vs. runtime complexity (speed)

Gradient boosting provided the best balance

FAST MODEL SCORING

Response < 100 milliseconds

JVM-based model (i.e. POJO)

Predictive power vs. runtime complexity (speed)

Gradient boosting provided the best balance

FAST MODEL SCORING

Response < 100 milliseconds

JVM-based model (i.e. POJO)

Predictive power vs. runtime complexity (speed)

Gradient boosting provided the best balance

FAST MODEL SCORING

VISITOR WEBSITE API MODEL DATA

Explore new technologies continuously

Ability to switch new models “on-the-fly”

Make the API faster

Incorporate new data sources

Resiliency, failover capabilities

Technology changes

Flexibility of the cloud

Keep it simple

Small empowered teams

THANK YOU