AgileML:一个包含主动消费者参与的机器学习项目开发管道

R. Shukla, J. Cartlidge
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引用次数: 3

摘要

机器学习(ML)项目部署通常需要很长时间,并且可能由于缺乏数据、数据质量差和数据漂移而面临延迟或失败。为了解决这些问题,我们引入了AgileML,这是一种新颖的机器学习产品开发生命周期,终端用户和开发团队通过迭代的开发过程协同工作。我们使用AgileML来开发商业支出分类服务,并证明最早的alpha部署可以为用户提供重要的商业价值。专业消费分析师的用户测试表明,该系统可以将分类速度提高五倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AgileML: A Machine Learning Project Development Pipeline Incorporating Active Consumer Engagement
Machine learning (ML) project deployments often have long lead times and may face delays or failures due to lack of data, poor data quality, and data drift. To address these problems, we introduce AgileML, a novel machine learning product development lifecycle where the end consumer and development team work collaboratively through an iterative process of development. We use AgileML to develop a commercial spend classification service and demonstrate that the earliest alpha deployment can offer users significant commercial value. User-testing with a professional spend analyst demonstrates that the system can lead to a five-fold increase in classification speed.
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