Data-Centric AI - Marysia Winkels

DataTalks.Club

We talked about:

  • Marysia’s background
  • What data-centric AI is
  • Data-centric Kaggle competitions
  • The mindset shift to data-centric AI
  • Data-centric does not mean you should not iterate on models
  • How to implement the data-centric approach
  • Focusing on the data vs focusing on the model
  • Resources to help implement the data-centric approach
  • Data-centric AI vs standard data cleaning
  • Making sure your data is representative
  • Knowing when your data is good enough
  • The importance of user feedback
  • “Shadow Mode” deployment
  • What to do if you have a lot of bad data or incomplete data
  • Marysia’s role at PyData
  • How Marysia joined PyData
  • The difference between PyData and PyCon
  • Finding Marysia online

Links:

  • Embetter & Bulk Demo: https://www.youtube.com/watch?v=L---nvDw9KU

Free data engineering course: https://github.com/DataTalksClub/data-engineering-zoomcamp

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html

To listen to explicit episodes, sign in.

Stay up to date with this show

Sign in or sign up to follow shows, save episodes, and get the latest updates.

Select a country or region

Africa, Middle East, and India

Asia Pacific

Europe

Latin America and the Caribbean

The United States and Canada