AutoML engineering practices and large-scale industry applications

Thunderbolt AI Research Service. AutoML is the hot topic of machine learning this year, and the technology has a lot of potential and can yield great value in engineering practice. At this stage, the industry is mainly in the stage of exploring the difficulties and directions of AutoML, and none of them has yet launched a systematic solution. In engineering practice, AutoML has yet to become a major enabler of automation and intelligence for machine learning processes. In practice, AutoML optimization for big models with big data is lacking in both RESEARCH and ENGINEERING.

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AutoML engineering practices and large-scale industry applications

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Xu Hao , a core algorithm engineer at Cloudbrain Technology and an expert in machine learning and high performance computing. He has more than ten years of R&D experience in machine learning, natural language processing, high performance distributed computing, graph computing, stochastic optimization, etc. D. in computer engineering from the University of Cincinnati. He has published more than 20 top IEEE/ACM papers and was awarded the only best paper award at the 8th International Conference on Low Energy Electronics. He was the principal engineer of ANSYS software and led the development of a distributed probabilistic graph computation system with 1 billion nodes.

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(Beijing time)Tuesday, December 04, 10:00 a.m.

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