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dl17:Debugging ML in Production feat
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Machine learning pipelines can successfully demonstrate high performance on train and evaluation datasets, but what happens after you promote that model to production? What are some of the challenges faced, and how do groups of different stakeholders with different technical abilities collaborate to identify and “fix” bugs? In my talk, I will draw from my experiences to describe a high level overview of modern ML infrastructure, criteria for promoting models, case studies of “bugs” encountered when clients were interacting with the live ML predictions, and the challenges in solving these issues. 深度学习调试指南 如需英文视频请移步油管:aGzu7nI8IRE #LuQuant#中文台译制片频道乐心出品! 学习英文文档靠谷歌翻译,学习英文视频靠LuQuant中文! 翻译质量Google全程背锅,中文配音LuQuant竭尽全力!
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