Search a title or topic

Over 20 million podcasts, powered by 

Player FM logo
Artwork

Content provided by The Data Bros and The Firebolt Data Bros. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by The Data Bros and The Firebolt Data Bros or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.
Player FM - Podcast App
Go offline with the Player FM app!

60 Billion Predictions Daily: Inside Credit Karma’s Agentic Data Layer with Maddie Daianu

19:55
 
Share
 

Manage episode 520205412 series 3418247
Content provided by The Data Bros and The Firebolt Data Bros. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by The Data Bros and The Firebolt Data Bros or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.
What does MLOps look like when you are deploying 22,000 models a month?
Maddie Daianu, Head of Data and AI at Intuit Credit Karma, joins the Data Bros to pull back the curtain on one of the most high-volume data environments in FinTech. With a 100-person team serving 140 million members, standard data practices break down.
Maddie shares how her team manages terabytes of daily data on Google Cloud and explains the massive strategic pivot they are undertaking right now: The move from "Information" to "Agency."
What You'll Learn:

  • Extreme Scale: How to architect a system that handles 80 billion daily predictions without latency.
  • The Unified Consumer Profile: The hackathon project that unlocked real-time personalization across Credit Karma and TurboTax.
  • The "Done-For-You" Future: Why they are building an "Agentic Data Layer" to move from recommending financial products to actively managing them for the user.
If you want to know what the future of high-scale AI infrastructure looks like, this is the blueprint.
If you enjoyed this episode, make sure to subscribe, rate, and review it on Apple Podcasts, Spotify, and YouTube Podcasts. Instructions on how to do this are here.

About the Guest(s)

Maddie Daianu is the Head of Data and AI at Intuit Credit Karma, where she leads the teams responsible for AI science, machine learning engineering, data engineering, and the experimentation platform. She brings a background that spans academic research in biomedical engineering and machine learning, and experience at both smaller companies and Meta. Her current focus is on building the data and AI infrastructure that drives highly personalized financial experiences for Credit Karma's 140 million members and contributes to Intuit's broader consumer ecosystem.

Quotes

"The key elements and ingredients of making this app successful is data and AI." - Maddie

"We have and we process and transform multiple terabytes of information daily for our 140,000,000 members every single day." - Maddie

"We have our models that essentially, lead to almost 60,000,000,000 daily predictions for our 140,000,000 member base every single day." - Maddie

"We want to take this to the next level. So Intuit as a whole believes... in creating done for you experiences for our users." - Maddie

"If you don't structure your data in a semantically, well structured way, you are not likely able to provide the most highly relevant and personalized experiences for users." - Maddie

"One thing that we've been building, in the last year or so it's called the unified consumer profile." - Maddie

"Intuit has been investing in tremendously over the last, few years... the generative AI operating system... to move fast and continuously disrupt ourselves, especially in the age of AI." - Maddie

Resources
Connect on LinkedIn:


Websites:


Tools & Platforms:

  • BigQuery – Data warehouse for processing multiple terabytes of information daily
  • Bigtable – Operational serving layer for real-time data access
  • Vertex AI – Machine learning platform for model training and deployment
  • Alchemy – Feature online feature store for real-time transformations and aggregations
  • Generative AI Operating System – Centralized platform for democratizing Gen AI adoption across Intuit products

Products & Services Mentioned:

  • TurboTax – Tax preparation and filing software
  • Debt Agent – AI-powered tool for debt consolidation and management assistance
  • Unified Consumer Profile – Semantic graph depicting financial journey across Credit Karma and TurboTax

The Data Engineering Show is brought to you by firebolt.io and handcrafted by our friends over at: fame.so
Previous guests include: Joseph Machado of Linkedin, Metthew Weingarten of Disney, Joe Reis and Matt Housely, authors of The Fundamentals of Data Engineering, Zach Wilson of Eczachly Inc, Megan Lieu of Deepnote, Erik Heintare of Bolt, Lior Solomon of Vimeo, Krishna Naidu of Canva, Mike Cohen of Substack, Jens Larsson of Ark, Gunnar Tangring of Klarna, Yoav Shmaria of Similarweb and Xiaoxu Gao of Adyen.
Check out our three most downloaded episodes:
  continue reading

64 episodes

Artwork
iconShare
 
Manage episode 520205412 series 3418247
Content provided by The Data Bros and The Firebolt Data Bros. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by The Data Bros and The Firebolt Data Bros or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.
What does MLOps look like when you are deploying 22,000 models a month?
Maddie Daianu, Head of Data and AI at Intuit Credit Karma, joins the Data Bros to pull back the curtain on one of the most high-volume data environments in FinTech. With a 100-person team serving 140 million members, standard data practices break down.
Maddie shares how her team manages terabytes of daily data on Google Cloud and explains the massive strategic pivot they are undertaking right now: The move from "Information" to "Agency."
What You'll Learn:

  • Extreme Scale: How to architect a system that handles 80 billion daily predictions without latency.
  • The Unified Consumer Profile: The hackathon project that unlocked real-time personalization across Credit Karma and TurboTax.
  • The "Done-For-You" Future: Why they are building an "Agentic Data Layer" to move from recommending financial products to actively managing them for the user.
If you want to know what the future of high-scale AI infrastructure looks like, this is the blueprint.
If you enjoyed this episode, make sure to subscribe, rate, and review it on Apple Podcasts, Spotify, and YouTube Podcasts. Instructions on how to do this are here.

About the Guest(s)

Maddie Daianu is the Head of Data and AI at Intuit Credit Karma, where she leads the teams responsible for AI science, machine learning engineering, data engineering, and the experimentation platform. She brings a background that spans academic research in biomedical engineering and machine learning, and experience at both smaller companies and Meta. Her current focus is on building the data and AI infrastructure that drives highly personalized financial experiences for Credit Karma's 140 million members and contributes to Intuit's broader consumer ecosystem.

Quotes

"The key elements and ingredients of making this app successful is data and AI." - Maddie

"We have and we process and transform multiple terabytes of information daily for our 140,000,000 members every single day." - Maddie

"We have our models that essentially, lead to almost 60,000,000,000 daily predictions for our 140,000,000 member base every single day." - Maddie

"We want to take this to the next level. So Intuit as a whole believes... in creating done for you experiences for our users." - Maddie

"If you don't structure your data in a semantically, well structured way, you are not likely able to provide the most highly relevant and personalized experiences for users." - Maddie

"One thing that we've been building, in the last year or so it's called the unified consumer profile." - Maddie

"Intuit has been investing in tremendously over the last, few years... the generative AI operating system... to move fast and continuously disrupt ourselves, especially in the age of AI." - Maddie

Resources
Connect on LinkedIn:


Websites:


Tools & Platforms:

  • BigQuery – Data warehouse for processing multiple terabytes of information daily
  • Bigtable – Operational serving layer for real-time data access
  • Vertex AI – Machine learning platform for model training and deployment
  • Alchemy – Feature online feature store for real-time transformations and aggregations
  • Generative AI Operating System – Centralized platform for democratizing Gen AI adoption across Intuit products

Products & Services Mentioned:

  • TurboTax – Tax preparation and filing software
  • Debt Agent – AI-powered tool for debt consolidation and management assistance
  • Unified Consumer Profile – Semantic graph depicting financial journey across Credit Karma and TurboTax

The Data Engineering Show is brought to you by firebolt.io and handcrafted by our friends over at: fame.so
Previous guests include: Joseph Machado of Linkedin, Metthew Weingarten of Disney, Joe Reis and Matt Housely, authors of The Fundamentals of Data Engineering, Zach Wilson of Eczachly Inc, Megan Lieu of Deepnote, Erik Heintare of Bolt, Lior Solomon of Vimeo, Krishna Naidu of Canva, Mike Cohen of Substack, Jens Larsson of Ark, Gunnar Tangring of Klarna, Yoav Shmaria of Similarweb and Xiaoxu Gao of Adyen.
Check out our three most downloaded episodes:
  continue reading

64 episodes

All episodes

×
 
Loading …

Welcome to Player FM!

Player FM is scanning the web for high-quality podcasts for you to enjoy right now. It's the best podcast app and works on Android, iPhone, and the web. Signup to sync subscriptions across devices.

 

Copyright 2025 | Privacy Policy | Terms of Service | | Copyright
Listen to this show while you explore
Play