Abstract
To grow their business and increase their audience, content distributors must understand the viewing habits and interests of content consumers. This typically requires solving tough computational problems, such as rapidly processing vast amounts of raw data from web sites, social media, devices, catalogs, and back-channel sources. Fortunately, today's content distributors can take advantage of the scalability, cost effectiveness, and pay-as-you-go model of the cloud to address these challenges. In this paper, we show content distributors how to use cloud technologies to build predictive analytic solutions. We examine architectural patterns for optimizing media delivery, and we discuss how to assess the overall consumer experience based on representative data sources. Finally, we present concrete implementations of cloud-based machine learning services and show how to use the services to profile audience demand, to cue content recommendations, and to prioritize the delivery of related media.
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