In order to establish a user base across the globe, a product needs to support a variety of locales. The challenge with supporting multiple locales is the maintenance and generation of localized strings, which are deeply integrated into many facets of a product. To address these challenges, Qordoba is using machine learning with highly scalable technologies such as Apache Spark to automate the process. Specifically, Qordoba needs to generate high-quality translations in many different languages and make them available in real time across platforms (e.g., mobile, print, and the web).
Michelle Casbon describes the techniques Qordoba uses to provide continuous deployment of localized strings, live syncing across platforms (mobile, web, photoshop, sketch, help desk, etc.), content generation for any locale, and emotional response. Michelle also explores Qordoba’s architecture for handling billions of localized strings in many different languages, explaining how Qordoba uses:
. . .all in a platform that makes it feasible to build products that feel native to every user, regardless of language.
Michelle Casbon is a senior engineer on the Google Cloud Platform developer relations team, where she focuses on open source contributions and community engagement for machine learning and big data tools. Michelle’s development experience spans more than a decade and has primarily focused on multilingual natural language processing, system architecture and integration, and continuous delivery pipelines for machine learning applications. Previously, she was a senior engineer and director of data science at several San Francisco-based startups, building and shipping machine learning products on distributed platforms using both AWS and GCP. She especially loves working with open source projects and is a contributor to Kubeflow. Michelle holds a master’s degree from the University of Cambridge.
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