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38. Building trusted ML products with Karin Schöfegger (Ex-Google & N26)

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Manage episode 358215939 series 2895967
Content provided by MOSTLY AI. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by MOSTLY AI 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://player.fm/legal.

Karin Schöfegger is a seasoned ML product manager who knows how to create successful AI/ML products and mitigate the risks involved. In this episode, she shares her insights about challenges in building AI products. Tune in to learn about:

  • How to align the business side and the data science side of product development
  • What's the difference between traditional software development and machine learning development
  • How to bring customer understanding into data science and engineering
  • What are the most common traps in data science
  • Why it's essential to work with realistic data instead of picture-perfect datasets
  • How to get buy-in from stakeholders for ethical AI
  continue reading

47 episodes

Artwork
iconShare
 
Manage episode 358215939 series 2895967
Content provided by MOSTLY AI. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by MOSTLY AI 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://player.fm/legal.

Karin Schöfegger is a seasoned ML product manager who knows how to create successful AI/ML products and mitigate the risks involved. In this episode, she shares her insights about challenges in building AI products. Tune in to learn about:

  • How to align the business side and the data science side of product development
  • What's the difference between traditional software development and machine learning development
  • How to bring customer understanding into data science and engineering
  • What are the most common traps in data science
  • Why it's essential to work with realistic data instead of picture-perfect datasets
  • How to get buy-in from stakeholders for ethical AI
  continue reading

47 episodes

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