Machine learning has found increasing use in the real world, and yet a framework for productionizing machine learning algorithms is lacking. This talk discusses how companies can bridge the gap between research and production in machine learning. It starts with the key differences between the research and production environments: data, goals, compute requirements, and evaluation metrics. It also breaks down the different phases of a machine learning production cycle, the infrastructure currently available for the process, and the industry best practices.
Huyen Nguyen (Stanford)
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