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引用次数: 0

摘要

在人工智能项目中应用敏捷是具有挑战性的,因为“早期和持续交付有价值的软件”的想法正在挑战自己。我们能否重新定义可以增量开发的有价值的输出?我们如何“分割”AI需求?我们如何评估人工智能项目?通过我们的咨询工作,我们与一些人工智能和数据工程团队合作,为这些挑战开发了一个敏捷解决方案。在本教程中,我们将讨论关于敏捷的误解,这些误解使得在AI项目中采用敏捷具有挑战性,以及如何在AI项目中实现敏捷。此外,我们还将介绍人工智能中的敏捷过程模型、处理人工智能需求、评估和计划的技巧。
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Applying Agile for AI projects
Applying Agile is AI projects is quit challenging, as the idea of "early and continuous delivery of valuable software" is challenging itself. Can we redefine valuable output that can be incrementally developed? How can we "slice" AI requirements? How Can we estimate AI projects? Through our work in consultation we had worked with some AI and Data Engineering teams to develop an Agile solution for these challenges. In this tutorial we will discuss the misconceptions about Agile that make adopting it in AI projects challenging, and how to be Agile in AI projects. Also, we will have a look on models for Agile process in AI, tips to handle AI Requirements, estimation, and planning as well.
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