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It is evident that when machine learning meets big data, it is vital to have a powerful system/infrastructure to support the distributed training task. In recent years, people have used different frameworks for this purpose, including iterative MapReduce, parameter server, and data flow. In this session, we are going to discuss how to enhance these frameworks from both system and algorithmic perspectives, and how to implement parallel machine learning algorithms under these frameworks. In addition, we will discuss the future trend of machine learning system and infrastructure and how to push its frontier through close collaboration between academia and industry.
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