Date post: | 15-Apr-2017 |
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Data & Analytics |
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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