Abstract: 

Organizing products using structured metadata is crucial in online retail. This metadata is usually needed by many downstream applications including search and discovery, trust and safety, analytics and reporting among others. At Shopify we like to make the commerce journey as easy as possible for our merchants and one part of this is using Machine Learning to predict the product category for the billions of products that our merchants sell.

We will look at how we solve this problem using transfer learning through Natural Language Processing and Computer vision to create a hierarchical classification Deep Neural Network to categorize products into a hierarchical tree taxonomy. We will dig deeper into modeling challenges and how we came up with specific architecture decisions. We will then dive into the various technology and tool choices we made in order to make this work at Shopify Scale. This section will also briefly go over some of the specific platform tooling that needed to be built and how this project pushed the boundaries of ML capabilities at Shopify. The talk will cover how we continuously monitor the performance of the model using both ML as well as business metrics and how this leads into a feedback mechanism that results in better models.

Finally we will talk about how all of this was built keeping merchant success front and center of all the product as well as technical decisions we made by talking about different features that are built on top of this model that have benefited our merchants.

Bio: 

Kshetrajna is a Staff Data Scientist at Shopify working in the Merchant Services Org. Over the last 10 years of his career he has built and productionalized many ML models in various domains including retail, ad-tech and healthcare. His interests are mainly applied ML and ML systems and enjoys solving complex problems to help use machine learning at scale. Outside of work, Kshetrajna loves to spend time with his dogs, play music on his guitar, and is an avid gamer.