To establish a global user base, a product needs to support a variety of locales. The challenge with supporting multiple locales is the maintenance and generation of localized strings. Michelle Casbon explains how open source tools like Scala, Apache Spark, Apache Kafka, and Apache PredictionIO (incubating) provide structure for a scalable localization platform with machine learning at its core.
Michelle explains how Qordoba has addressed these challenges using highly scalable technologies and machine learning to automate the process, specifically, by generating high-quality translations in many different languages and making them available in real-time across platforms (e.g., mobile, print, and web). Such a platform offers 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 shares Qordoba’s architecture for handling billions of localized strings in many different languages, which uses:
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 has contributed to Apache Spark and Apache Flume. Her writing has been featured in the AI section of O’Reilly Radar. Michelle holds a master’s degree from the University of Cambridge.
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