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What It Takes to Productize Next-Gen AI on a Global Scale (Srujana Kaddevarmuth, Senior Director of Data & Machine Learning Programs, Walmar

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Content provided by Domino Data Labs. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Domino Data Labs 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://ro.player.fm/legal.
What does it take to turn the latest advances in AI into products that deliver business impact at Walmart levels of global scale?
Srujana Kaddevarmuth is the Senior Director of Data & Machine Learning Programs at Walmart Global Tech. Her team drives data strategy and grapples with data science productization every day. With millions of employees, hundreds of millions of customers, and petabytes of data at any given moment, Walmart offers some unique lessons in the complexities of building teams, processes, and products to effectively leverage AI at scale.
In this episode, Srujana shares a few of those lessons, along with her perspective on nonlinear career paths, organizational collaboration and alignment, and her ongoing fascination with what’s next. Plus, she dives into her passion for fostering diversity in data science and tech, sharing strategies leaders can implement to help bring more women into the field.
We discuss:
What to prioritize when experimenting with next-gen tech
How to use “communities of practice” to align your organization
Solving governance, reproducibility, and knowledge sharing challenges at scale
Bringing more women into data science
In this season finale episode, host Dave Cole also shares his three biggest takeaways from his many in-depth conversations with leaders in data science.
Stay tuned for a whole new season of Data Science Leaders coming soon! We're just getting started.
Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.
  continue reading

72 episoade

Artwork
iconDistribuie
 
Manage episode 330257331 series 3279398
Content provided by Domino Data Labs. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Domino Data Labs 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://ro.player.fm/legal.
What does it take to turn the latest advances in AI into products that deliver business impact at Walmart levels of global scale?
Srujana Kaddevarmuth is the Senior Director of Data & Machine Learning Programs at Walmart Global Tech. Her team drives data strategy and grapples with data science productization every day. With millions of employees, hundreds of millions of customers, and petabytes of data at any given moment, Walmart offers some unique lessons in the complexities of building teams, processes, and products to effectively leverage AI at scale.
In this episode, Srujana shares a few of those lessons, along with her perspective on nonlinear career paths, organizational collaboration and alignment, and her ongoing fascination with what’s next. Plus, she dives into her passion for fostering diversity in data science and tech, sharing strategies leaders can implement to help bring more women into the field.
We discuss:
What to prioritize when experimenting with next-gen tech
How to use “communities of practice” to align your organization
Solving governance, reproducibility, and knowledge sharing challenges at scale
Bringing more women into data science
In this season finale episode, host Dave Cole also shares his three biggest takeaways from his many in-depth conversations with leaders in data science.
Stay tuned for a whole new season of Data Science Leaders coming soon! We're just getting started.
Tune in on Apple Podcasts, Spotify, our website, or wherever you listen to podcasts.
Can’t see the links above? Just visit domino.buzz/podcast for helpful links from each episode.
  continue reading

72 episoade

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