Moving From Model-centric to Data-centric Artificial Intelligence

A talk by Anurag Singh
Senior Artificial Intelligence Engineer, Kimberly-Clark

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About this talk

Even with rising adoption, the failure rate for AI projects is incredibly high. How can we change that?

The basic components of all AI systems are data and model, both go hand in hand in producing desired results. We are here to talk about how the AI community has been biased towards putting more effort in the model, and see how it is not always the best approach. The value of data and its impact on the quality of ML-based solutions have, for sure, been underestimated so far, but this is changing Andrew NG recently announced data-centric AI competition where he covered the benefits of a bigger investment in data preparation with his team proving that investing in improved existing data quality is effective as collecting the triple amount of the data.

Come Join us and discover:

  • 1) Difference between Model Centric and Data Centric approaches and the benefits of the paradigm shift.
  • 2) Trends in the AI community
  • 3) Importance of data
  • 4) Systematic data centric approach
  • 5) Essential capabilities to look for in adoption of the data centric approach.

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