Machine learning offers great potential to developers and end users in the creative industries. For example, it can support new sensor-based interactions, procedural content generation and enduser product customisation. However, designing machine learning toolkits for adoption by creative developers is still a nascent effort. This work focuses on the application of user-centred design with creative end-user developers for informing the design of an interactive machine learning toolkit. We introduce a framework for user-centred design actions that we developed within the context of an European Union innovation project, RAPID-MIX. We illustrate the application of the framework with two actions for lightweight formative evaluation of our toolkit—the JUCE Machine Learning Hackathon and the RAPID-MIX API workshop at eNTERFACE’17. We describe how we used these actions to uncover conceptual and technical limitations. We also discuss how these actions provided us with a better understanding of users, helped us to refine the scope of the design space, and informed improvements to the toolkit. We conclude with a reflection about the knowledge we obtained from applying user-centred design to creative technology, in the context of an innovation project in the creative industries.

中文翻译：

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以用户为中心的设计动作，用于轻量级评估交互式机器学习工具包

机器学习为创意产业的开发人员和最终用户提供了巨大的潜力。例如，它可以支持新的基于传感器的交互，过程内容生成和最终用户产品定制。但是，设计机器学习工具包以供创意开发人员采用仍然是一项新生的工作。这项工作着重于以用户为中心的设计与富有创造力的最终用户开发人员的结合，以为交互式机器学习工具包的设计提供信息。我们介绍了一个以用户为中心的设计行动框架，该框架是在欧盟创新项目RAPID-MIX的背景下开发的。我们通过两个动作来说明该框架的应用，以对我们的工具包进行轻量级的格式评估-JUCE机器学习黑客马拉松和eNTERFACE'17的RAPID-MIX API研讨会。我们描述了如何使用这些动作来发现概念和技术上的限制。我们还将讨论这些操作如何使我们对用户有更好的了解，如何帮助我们完善设计空间的范围，以及如何对工具包进行明智的改进。在创意产业中的创新项目的背景下，我们对通过将以用户为中心的设计应用于创意技术而获得的知识进行了总结。