Eliminating Garbage In/Garbage Out for Analytics and ML

Eliminating Garbage In/Garbage Out for Analytics and ML

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[] Takeaways

3 of 16

3 of 16

[] Takeaways

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Eliminating Garbage In/Garbage Out for Analytics and ML

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  1. 1 [] Santona's and Roy's preferred coffee
  2. 2 [] Santona's and Roy's background
  3. 3 [] Takeaways
  4. 4 [] Please like, share, and subscribe to our MLOps channels!
  5. 5 [] Back story of having Santona and Roy on the podcast
  6. 6 [] Santona's story
  7. 7 [] Optimal tag teamwork
  8. 8 [] Dealing with stakeholder needs
  9. 9 [] Having mechanisms in place
  10. 10 [] Building for data Engineers vs building for data scientists
  11. 11 [] Creating solutions for users
  12. 12 [] User experience holistic point of view
  13. 13 [] Tooling sprawl is real
  14. 14 [] LLMs reliability
  15. 15 [] Things would have loved to learn five years ago
  16. 16 [] Wrap up

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