Archana Sapkota

Archana is a machine learning (ML) manager at Wayfair who loves working in the field of ML (Computer Vision, NLP, Multimodal) and finding solutions that help make lives better. She leads automation of catalog tagging to make sure the information about products in the catalog is rich and clean- a crucial task to help customers find what they are looking for via search, filter etc. Within Wayfair, she is recognized as a subject matter expert for computer vision applications. Overall, Archana has more than 8 years of experience in research and development of machine learning algorithms and bringing them into production in the field of retail, biometrics, consumer photos and smart energy.

A picture of a yellow couch with accent pillows and a coffee table can be used to extract product tags, like "square arm", "sofa", "vintage modern" style, etc. for the couch, and "table", "round", "black", "4 legs" for the coffee table.

Wayfair Tech Blog

Accelerating Catalog Tagging Automation with Snorkel’s Data-Centric AI Platform: Wayfair’s Success Story

One of our missions at Wayfair is to help our 22 million customers find the products they are looking for. For example, when a customer searches for a “modern yellow sofa” on Wayfair, we want to show the most relevant options from the tens of thousands of sofas available in our catalog. To do that, it is important to have a strong understanding of the products we sell. We use machine learning algorithms to analyze and understand the descriptive information (e.g. color, shape) of over 40 million products provided by more than 20 thousand suppliers in our catalog.

10 Min Read

A picture of a grey chair is broken down into three colors, occurring at 55%, 42%, and 3%. When put into the color taxonomy, the three colors are identified as Taupe Grey, Dark Grey, and Pitch black, respectively. Knowing this, the chair can be classified as 97% grey and 3% black

Wayfair Tech Blog

From RGB to Descriptive Color Names: Wayfair's in-house color algorithms to improve customer shopping experience.

When a Wayfair customer shops for a product that is just right for her home, color plays a big role. For example, a customer might say, “I am looking for a green chair.” Or she might say, “I am looking for a teal chair.” There may be various versions of these customer color search stories. As data scientists at Wayfair, we want to solve these customer problems by algorithmically extracting the color information from the products and assigning customer-friendly color names.

10 Min Read