Data Centric AI is the term coined by AI pioneer Andrew Ng for the movement that argues we shift our focus towards iterating on our data instead of models to improve machine learning predictions. But isn't this what we have always done? Why is this trend relevant now? Has something really changed, and if so, how does that change your work as a data scientist?
This talk will feature anecdotes and real-world examples of 'model-itis' that serve as an argument for data-centric AI, our lessons learned from winning the Data Centric AI competition, and practical tips on how you can integrate data-centric principles in your daily work.