If you care about delivering value with data science you should probably care about Data-Centric AI.
Data-Centric AI is about iterating on the data instead of the model architecture to create good machine learning models.
Read the full thread on Twitter where I explain why this approach matters for practical applications.
Proud to announce that my team was one of the winners in Andrew Ng's Data-Centric AI Competition! This competition focused on improving model performance by enhancing the dataset rather than changing the model itself.
To learn more about our approach and the data-centric techniques we used, check out our detailed blog post where we share the three key tips that helped us succeed.
We also shared a blogpost on DeepLearning.AI about our experience with the competition.
This blog was originally posted at Xebia.com, my employer at the time of writing.
Andrew Ng (co-founder of Coursera, Google Brain, deeplearning.ai, landing.ai) is most famous for his Machine Learning course on Coursera. It teaches the basics of machine learning, how to create models and how to use them to predict with great accuracy.
Recently, he has introduced the concept of Data-Centric AI. The idea is that rather than treating your dataset as fixed and solely focus on improving your model setup, it focuses on improving your dataset. He argues that this is often much more effective to improve your performance.