Data Engineering

How Wayfair leverages Graph Neural Networks to thwart account-hopping fraudsters

Dive into the ways Wayfair’s data scientists are using Vertex AI to improve model productionization, serving, and operational readiness velocity.

Today, many companies rely heavily on third-party event tracking platforms for collecting event data. Perhaps the most well-known of these is Google Analytics. However, at Wayfair, we chose to go another route, largely eschewing the third-party vendor model to develop our own first-party event tracking platform. It’s called Scribe.

Suppliers are an integral part of Wayfair, and providing them with real-time and high-quality data with flexibility is important for our mission. Today I’m going to highlight two specific areas: how Wayfair is leveraging real-time data to provide suppliers with up-to-date sales information and how our team has tackled some of the problems that emerged along the journey.

If you are among the tens of millions of customers who have shopped at Wayfair, you have experienced one of the many technologies built by the Machine Learning and Data Platforms team. We serve the business, engineering and data science teams across Wayfair, who are working on solving one of the most intriguing and gratifying challenges — helping our customers make their dream home a reality.

At Wayfair, we recommend millions of products across all styles and budgets. Because of the scale of our catalog, we constantly ask ourselves: how can we help our customers find the right products efficiently while discovering exciting complements to fill out their space?