Jeff Smith builds large-scale machine learning systems using Scala and Spark. For the past decade, he has been working on data science applications at various startups in New York, San Francisco, and Hong Kong. He blogs and speaks about various aspect of building real world machine learning systems.
Machine learning applications autonomously reason about data at massive scale. It’s important that they remain responsive in the face of failure and changes in load. And the best way to to keep applications responsive, resilient, and elastic is to incorporate reactive design. But machine learning systems are different than other applications when it comes to testing, building, deploying, and monitoring. They also have unique challenges when you need to change the semantics or architecture of the system. To make machine learning systems reactive, you need to understand both reactive design patterns and modern data architecture patterns.
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写 ps 参考用 跟博客差不多
评分写 ps 参考用 跟博客差不多
评分写 ps 参考用 跟博客差不多
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评分写 ps 参考用 跟博客差不多
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