StackAdapt on Efficient Analytics and Machine Learning for Trillions of Records Using AWS
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26 дней – 9706:52Опубликовано 26 сентября 2018, 20:50
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StackAdapt CTO Yang Han, Director of Engineering Afish Mahesaniya, and Manager, Data Science Ned Dimitrov present how StackAdapt auto-scaled over 500,000 requests per second, and handles analytics across trillions of event-based data using Redshift, Lambda, and Kinesis. They also showcase how they used EMR for machine learning across billions of historical records and 50,000 features.
StackAdapt CTO Yang Han, Director of Engineering Afish Mahesaniya, and Manager, Data Science Ned Dimitrov present how StackAdapt auto-scaled over 500,000 requests per second, and handles analytics across trillions of event-based data using Redshift, Lambda, and Kinesis. They also showcase how they used EMR for machine learning across billions of historical records and 50,000 features.
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