In earlier installments we covered how our cloud journey started, talked about testing and validation, and discussed internal processes that positioned us for making rapid iterations and helped to achieve predictable delivery.

Why we decided to modularize

High-quality lifestyle imagery is core to Wayfair’s ability to merchandise its catalog effectively and help customers shop confidently. Creating lifestyle images through 3D models enables rapid production of product visual merchandising at scale, drives simultaneous reduction in cost and speed to produce imagery, and unlocks new ways (e.g. AR, VR) for customers to engage with products. Early tests show 3D-generated images can provide high quality visual detail of our products and increase conversion rates for flagship brands (main products in Wayfair’s portfolio and to which customers most relate or identify Wayfair). Therefore, producing 3D models quickly and accurately is key to further reducing 3D imagery costs and driving revenue. In this blog, we will introduce how machine learning is utilized to aid 3D modeling at Wayfair.

This article is part of our series "Employee Spotlight," where we speak to and highlight some of the wonderful people who make up the exceptional teams at Wayfair.

As our journey continues, we have already covered testing at a smaller scale, learning throughout the process, and setting our ambitious goal to move Wayfair Storefront to the cloud!

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?

Wayfair is an international company with a presence in multiple countries: US, Canada, UK, and Germany. And where our UK customer might look to purchase a “heater”, our US customer might use the term “radiator” to describe the same thing. We want to ensure we’re able to understand that both customers are referring to the same item and interpret feedback left by customers in the form of product reviews correctly.

In the previous installment, we talked about testing smaller components first, expanding the scope gradually, and learning more about the platform of choice in the process.

In early August, the Data Science (DS) team put on their first DS-wide hackathon. The global hackathon event brought together Boston and Berlin based data scientists for one day of hacking and demos. The event challenged teams to think big about how algorithms can transform Wayfair’s business and help unveil unseen possibilities. Some successful tools and products on our site, like our Visual Search tool, started as hackathon projects! The hackathon is a fun challenge to try out new methods or algorithms, explore a different part of the business, and collaborate with team members across disciplines.

Have you ever wondered how engineering teams tackle the challenge of ensuring reliability and performance amid increasing site traffic? If so, I hope the information in this four-part series will be of interest to you.