eBay has released Krylov, their new artificial intelligence (AI) platform that allows the e-commerce giant to reach new capabilities. Some of these are improved language translation services and searching with images.
According to eBay’s Sanjeev Katariya, the vice president and chief architect of the eBay AI and platforms, and Ashok Ramani, director of product management, computer vision, natural and language processing, Krylov has changed the inner workings of eBay and the way users interact with the site.
“With computer vision powered by eBay’s modern AI platform, the technology helps you find items based on the click of your camera or an image. Users can go onto the eBay app and take a photo of what they are looking for and within milliseconds, the platform surfaces items that match the image,” Katariya and Ramani wrote in December.
“The user has not only activated computer vision technology, but they have also tapped into some advanced AI capabilities, including deep learning, distributed training and inferencing. The computer vision algorithm sifts through more than half a billion images and eBay’s 1.4 billion listings to find and show you the most relevant listings that are visually similar.”
Krylov is named after the mathematician Nicolai Krylov, and the project evolved over time, especially after eBay was at a point in which the site had a lot of data.
According to Katariya and Ramani, Krylov is utilized by data scientists at eBay to run thousands of model training experiments per month. Those revolve around different AI use cases including computer vision, natural language processing, merchandising recommendations, buyer personalization, seller price guidance, risk, trust, shipping estimates and more.
Prior to the development of Krylov, data scientists at eBay would build models to test new features for the site. Not only would these take weeks and months to finish, but they also wasted time, money, and energy on models that needed to be developed fast.
The new AI platform allows eBay’s scientists to automate model training and use the models individually or a common inference as a platform, and this only takes days instead of months.
Krylov is being used to improve eBay’s recommendation system, and it has enabled new image search features. These new features change the way a user searches the site. Users are now able to upload a photo of what they want, and the site will show them similar items.
“Krylov allows our AI teams to maximize the power of the vast repositories of data, both batch and real time, that eBay has. If you think of data as the fuel for artificial intelligence and machine learning, Krylov is the sophisticated vehicle being powered by that fuel,” Katariya and Ramani wrote.
The AI platform has been an important part of eBay’s machine translation technology, which is responsible for enabling cross-border trade. This makes up 60% of eBay’s international revenue.
Because of its many benefits and possibilities, various different departments worked on Krylov. Some worked on engineering in ads, computer vision, NLP, risk, trust and marketing.
“We had so many engineers and scientists across the company who needed help creating models and pushing out their models to production. We needed a complete closed loop on life cycle management of machine learning algorithms that was obvious. We needed a unified AI platform to really bring data scientists and engineers, modes and management experimentation all together,” Katariya said.
“If I look back on how we have progressed, I’m super proud of the collaboration, of the transparency, of the internal open source and how the training and education has gone above and beyond to build a really powerful platform that is global and deals with the scale of eBay,” he said.
“Krylov took a while to come of age but the objective was clear, that it was to ensure that our engineers and scientists across the globe, no matter where they were, were capable of accessing the right data at the right time, be it real time or batch-orientated data lakes or data warehouses or transactional data in a programmatic fashion.”
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