Amp, the new live radio app from Amazon, is a reinvention of radio featuring human-curated live audio shows. It’s designed to provide a seamless customer experience to listeners and creators by debuting interactive live audio shows from your favorite artists, radio DJs, podcasters, and friends.
However, as a new product in a new space for Amazon, Amp needed more relevant data to inform their decision-making process. Amp wanted a scalable data and analytics platform to enable easy access to data and perform machine leaning (ML) experiments for live audio transcription, content moderation, feature engineering, and a personal show recommendation service, and to inspect or measure business KPIs and metrics.
This post is the first in a two-part series. Part 1 shows how data was collected and processed using the data and analytics platform, and Part 2 shows how the data was used to create show recommendations using Amazon SageMaker, a

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