ºÝºÝߣshows by User: shriyaarora1 / http://www.slideshare.net/images/logo.gif ºÝºÝߣshows by User: shriyaarora1 / Wed, 14 Dec 2016 19:47:24 GMT ºÝºÝߣShare feed for ºÝºÝߣshows by User: shriyaarora1 Streaming datasets for personalization /slideshow/streaming-datasets-for-personalization/70148512 streamingdatasetsforpersonalization-161214194725
Streaming applications have historically been complex to design and implement because of the significant infrastructure investment. However, recent active developments in various streaming platforms provide an easy transition to stream processing, and enable analytics applications/experiments to consume near real-time data without massive development cycles.In this session, we will present our experience on stream processing unbounded datasets in the personalization space. The datasets consisted of -- but were not limited to -- the stream of playback events that are used as feedback for all personalization algorithms. These datasets when ultimately consumed by our machine learning models, directly affect the customer’s personalized experience. We’ll talk about the experiments we did to compare Apache Spark and Apache Flink, and the challenges we faced.]]>
Streaming applications have historically been complex to design and implement because of the significant infrastructure investment. However, recent active developments in various streaming platforms provide an easy transition to stream processing, and enable analytics applications/experiments to consume near real-time data without massive development cycles.In this session, we will present our experience on stream processing unbounded datasets in the personalization space. The datasets consisted of -- but were not limited to -- the stream of playback events that are used as feedback for all personalization algorithms. These datasets when ultimately consumed by our machine learning models, directly affect the customer’s personalized experience. We’ll talk about the experiments we did to compare Apache Spark and Apache Flink, and the challenges we faced.]]> Wed, 14 Dec 2016 19:47:24 GMT /slideshow/streaming-datasets-for-personalization/70148512 shriyaarora1@slideshare.net(shriyaarora1) Streaming datasets for personalization shriyaarora1 Streaming applications have historically been complex to design and implement because of the significant infrastructure investment. However, recent active developments in various streaming platforms provide an easy transition to stream processing, and enable analytics applications/experiments to consume near real-time data without massive development cycles.In this session, we will present our experience on stream processing unbounded datasets in the personalization space. The datasets consisted of -- but were not limited to -- the stream of playback events that are used as feedback for all personalization algorithms. These datasets when ultimately consumed by our machine learning models, directly affect the customer’s personalized experience. We’ll talk about the experiments we did to compare Apache Spark and Apache Flink, and the challenges we faced. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/streamingdatasetsforpersonalization-161214194725-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Streaming applications have historically been complex to design and implement because of the significant infrastructure investment. However, recent active developments in various streaming platforms provide an easy transition to stream processing, and enable analytics applications/experiments to consume near real-time data without massive development cycles.In this session, we will present our experience on stream processing unbounded datasets in the personalization space. The datasets consisted of -- but were not limited to -- the stream of playback events that are used as feedback for all personalization algorithms. These datasets when ultimately consumed by our machine learning models, directly affect the customer’s personalized experience. We’ll talk about the experiments we did to compare Apache Spark and Apache Flink, and the challenges we faced.
Streaming datasets for personalization from Shriya Arora
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