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

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The document discusses building real-time targeting capabilities at Capital One. It introduces two speakers, Ryan Zotti and Subbu Thiruppathy, and describes challenges around striving for speed in everything. It then covers how to achieve fast model data, training, deployment, and scoring through techniques like using the most up-to-date data, distributed computing in the cloud, automatic model refitting, and response times under 100 milliseconds.

Données & analyses

Building Real Time Targeting Capabilities
Capital One | Fast Marketing
July 20, 2016 | H20 Open Tour | NYC

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

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.jpg
https://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

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

Response < 100 milliseconds
JVM-based model (i.e. POJO)
Predictive power vs. runtime complexity (speed)
Gradient boosting provided the best balance
FAST MODEL SCORING

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

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Pascal Pfeiffer, Principal Data Scientist, H2O.ai H2O Open Source GenAI World SF 2023 This talk dives into the expansive ecosystem of Large Language Models (LLMs), offering practitioners an insightful guide to various relevant applications, from natural language understanding to creative content generation. While exploring use cases across different industries, it also honestly addresses the current limitations of LLMs and anticipates future advancements.

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Numerai is an open, crowd-sourced hedge fund powered by predictions from data scientists around the world. In return, participants are rewarded with weekly payouts in crypto. In this talk, Joe will give an overview of the Numerai tournament based on his own experience. He will then explain how he automates the time-consuming tasks such as testing different modelling strategies, scoring new datasets, submitting predictions to Numerai as well as monitoring model performance with H2O Driverless AI and R.

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In this session, you will learn about what you should do after you’ve taken an AI transformation baseline. Over the span of this session, we will discuss the next steps in moving toward AI readiness through alignment of talent and tools to drive successful adoption and continuous use within an organization. To find additional videos on AI courses, earn badges, join the courses at H2O.ai Learning Center: https://training.h2o.ai/products/ai-foundations-course To find the Youtube video about this presentation: https://youtu.be/K1Cl3x3rd8g Speaker: Chemere Davis (H2O.ai - Senior Data Scientist Training Specialist)

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The chances of successfully implementing AI strategies within an organization significantly improve when you can recognize where your organization is on the maturity scale. Over this course, you will learn the keys to unlocking value with AI which include asking the right questions about the problems you are solving and ensuring you have the right cross-section of talent, tools, and resources. By the end of this module, you should be able to recognize where your organization is on the AI transformation spectrum and identify some strategies that can get you to the next stage in your journey. To find additional videos on AI courses, earn badges, join the courses at H2O.ai Learning Center: https://training.h2o.ai/products/ai-foundations-course To find the Youtube video about this presentation: https://youtu.be/PJgr2epM6qs Speakers: Chemere Davis (H2O.ai - Senior Data Scientist Training Specialist) Ingrid Burton (H2O.ai - CMO)

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Machine Learning Model Deployment and Scoring on the Edge with Automatic Machine Learning and Data Flow YouTube Video URL: https://youtu.be/gB0bTH-L6DE Deploying Machine Learning models to the edge can present significant ML/IoT challenges centered around the need for low latency and accurate scoring on minimal resource environments. H2O.ai's Driverless AI AutoML and Cloudera Data Flow work nicely together to solve this challenge. Driverless AI automates the building of accurate Machine Learning models, which are deployed as light footprint and low latency Java or C++ artifacts, also known as a MOJO (Model Optimized). And Cloudera Data Flow leverage Apache NiFi that offers an innovative data flow framework to host MOJOs to make predictions on data moving on the edge.

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From Rapid Prototypes to an end-to-end Model Deployment: an AI Hedge Fund Use...

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

1. Building Real Time Targeting Capabilities Capital One | Fast Marketing July 20, 2016 | H20 Open Tour | NYC

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

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

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

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

8. MODEL DATA MODEL DEPLOYMENT MODEL SCORING MODEL TRAINING Our challenge is… …striving to be fast at everything

9. Most current, up-to-date data Available as soon as it’s ready Low latency at scale FAST MODEL DATA

10. Most current, up-to-date data Available as soon as it’s ready Low latency at scale FAST MODEL DATA

11. Most current, up-to-date data Available as soon as it’s ready Low latency at scale FAST MODEL DATA

12. Most current, up-to-date data Available as soon as it’s ready Low latency at scale FAST MODEL DATA

13. Distributed computing to crunch data fast Elastic scaling with the public cloud Speed from parallelism FAST MODEL TRAINING

14. Distributed computing to crunch data fast Elastic scaling with the public cloud Speed from parallelism FAST MODEL TRAINING

15. Distributed computing to crunch data fast Elastic scaling with the public cloud Speed from parallelism FAST MODEL TRAINING

16. Distributed computing to crunch data fast Elastic scaling with the public cloud Speed from parallelism FAST MODEL TRAINING

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. Model adapts to evolving customer landscape Automatically refit the model and daily deploy Seamlessly integrate with existing Java tech stack FAST MODEL DEPLOYMENT

19. Model adapts to evolving customer landscape Automatically refit the model and daily deploy Seamlessly integrate with existing Java tech stack FAST MODEL DEPLOYMENT

20. Model adapts to evolving customer landscape Automatically refit the model and daily deploy Seamlessly integrate with existing Java tech stack FAST MODEL DEPLOYMENT

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. 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. 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. Response < 100 milliseconds JVM-based model (i.e. POJO) Predictive power vs. runtime complexity (speed) Gradient boosting provided the best balance FAST MODEL SCORING

25. Response < 100 milliseconds JVM-based model (i.e. POJO) Predictive power vs. runtime complexity (speed) Gradient boosting provided the best balance FAST MODEL SCORING

26. VISITOR WEBSITE API MODEL DATA

27. Explore new technologies continuously Ability to switch new models “on-the-fly” Make the API faster Incorporate new data sources Resiliency, failover capabilities

28. Technology changes Flexibility of the cloud Keep it simple Small empowered teams

29. THANK YOU

Notes de l'éditeur

BOTH
Websites have evolved over time. And so has our website, check out what it looks like today! SUBBU
Capital One’s Credit Card Homepage has changed over time, so have our data and modeling needs SUBBU How can we make the card home page experience better Today everyone sees the same experience Future experience needs to be fast and targeted
How fast is fast data? RYAN
Tesla travels 0-60 MPH in 3.2 seconds RYAN
A blink of an eye takes 300-400 milliseconds RYAN
RYAN
RYAN
RYAN
RYAN
RYAN
RYAN
RYAN
RYAN
Solution: Sparking Water RYAN
RYAN
RYAN
RYAN
RYAN
RYAN
RYAN
RYAN
RYAN
RYAN
SUBBU
WHERE ARE WE GOING NEXT? SUBBU
Lessons Learned SUBBU Simple: network hops? Tech changes: Cloud: consumers are on cloud (faster vs on-prem), developer empowerment, on demand servers Small teams:

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

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

Notes de l'éditeur