Jul 17, 2020
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.
The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.
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