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Cloud AutoML - Custom Machine Learning Models  |  Google Cloud Platform

Last updated: 03-15-2019

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Cloud AutoML - Custom Machine Learning Models  |  Google Cloud Platform

“Cloud AutoML’s technology is helping us build vision models to annotate our products with Disney characters, product categories, and colors. These annotations are being integrated into our search engine to enhance the impact on Guest experience through more relevant search results, expedited discovery, and product recommendations on shopDisney.”

“Urban Outfitters is constantly looking for new ways to enhance our customers’ shopping experience. Creating and maintaining a comprehensive set of product attributes is critical to providing our customers relevant product recommendations, accurate search results, and helpful product filters; however, manually creating product attributes is arduous and time-consuming. To address this, our team has been evaluating Cloud AutoML to automate the product attribution process by recognizing nuanced product characteristics like patterns and neck lines styles. Cloud AutoML has great promise to help our customers with better discovery, recommendation, and search experiences.”

“ZSL is an international conservation charity devoted to the worldwide conservation of animals and their habitats. A key requirement to deliver on this mission is to track wildlife populations to learn more about their distribution and better understand the impact humans are having on these species. In order to achieve this, ZSL has deployed a series of camera traps in the wild that take pictures of passing animals when triggered by heat or motion. The millions of images captured by these devices are then manually analysed and annotated and with the relevant species such as elephants, lions, and giraffes, etc., which is a labour-intensive and expensive process. ZSL’s dedicated Conservation Technology Unit has been collaborating closely with Google’s CloudML team to help shape the development of this exciting technology, which ZSLaims to use to automate the tagging of these images – cutting costs, enabling wider-scale deployments, and gaining a deeper understanding of how to conserve the world’s wildlife effectively.”


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