Developing Machine Learning for Impact

Developing Machine Learning for Impact

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Why this talk?

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Why this talk?

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Developing Machine Learning for Impact

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  1. 1 Intro
  2. 2 Why this talk?
  3. 3 ML as a tool, not a solution
  4. 4 Less is often more
  5. 5 Without data quality, there is no ML
  6. 6 Surviving the ML uncertainty
  7. 7 The importance of multidisciplinary teams
  8. 8 What is not measured can't be improved
  9. 9 Success stories
  10. 10 Outro

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