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中国 Cassandra 技术社区
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Netflix Recommendations Using Spark + Cassandra

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发表于 2019-5-20 16:22:01 | 显示全部楼层 |阅读模式
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本ppt介绍了 Netflix 使用 Apache Spark(Streaming)和Apache Cassandra构建用于离线实验的时间机器和在线推荐的实时基础架构,使我们能够将数据大小扩展一个数量级,并在更短的时间内训练和验证模型。 我们将深入研究架构,用例细节,用于cassandra的数据模型并分享我们的学习经验。

Learning is an analytic process of exploring the past in order to predict the future. Hence, being able to travel back in time to create features is critical for machine learning projects to be successful. To enable this, we built a time machine that computes features for any arbitrary time in the recent past for offline experimentation. We also built a real-time stream processing system to capture the interests of members during different times of the day and to quickly adapt to changes in the collective interests of members as it happens in case of real-world events.
Building the time machine for offline experimentation and the real-time infrastructure for online recommendations with Apache Spark (Streaming) and Apache Cassandra empowered us to both scale up the data size by an order of magnitude and train and validate the models in less time. We will delve into the architecture, use case details, data models used for cassandra and share our learnings.


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发表于 2019-6-24 10:33:36 | 显示全部楼层
也进入Spark时代了,https://www.jianshu.com/p/9fe5c45411e4  
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