Operationalizing User-Provided Labels in Data-Centric AI

Today we’re back with another installment of our Data-Centric AI series, joined by Wendy Foster, a director of engineering & data science at Shopify. In our conversation with Wendy, we explore the differences between data-centric and model-centric approaches and how they manifest at Shopify, including on her team, which is responsible for utilizing merchant and product data to assist individual vendors on the platform. We discuss how they address, maintain, and improve data quality, emphasizing the importance of coverage and “freshness” data when solving constantly evolving use cases. Finally, we discuss how data is taxonomized at the company and the challenges that present themselves when producing large-scale ML models, future use cases that Wendy expects her team to tackle, and we briefly explore Merlin, Shopify’s new ML platform (that you can hear more about at TWIMLcon!), and how it fits into the broader scope of ML at the company.

The complete show notes for this episode can be found at twimlai.com/go/592