To guide ML practitioners through the development life cycle, the Cross-Industry Standard Process for the development of Machine Learning applications with Quality assurance methodology (CRISP-ML(Q)) was recently proposed. We review the core phases of the ML development process model (see Figure 1).

2020年3月11日 · The process model expands on CRISP-DM, a data mining process model that enjoys strong industry support but lacks to address machine learning specific tasks. Our work proposes an industry and application neutral process model tailored for machine learning applications with focus on technical tasks for quality assurance.

2024年11月25日 · The CRISP-ML (Q) framework, an adaptation of the well-known CRISP-DM (Cross-Industry Standard Process for Data Mining), is a structured approach for executing machine learning (ML) projects....

2024年1月16日 · CRISP-ML (Q) stands for Cross-Industry Standard Process for Data Mining with Quality Assurance. It’s a structured methodology for developing machine learning applications,...

在本文中，我们介绍了 crisp-ml(q) 开发生命周期模型。总体而言，crisp-ml(q) 是一种用于机器学习软件开发的系统过程模型，它创建对可能风险的认识并强调质量保证以减少这些风险以确保 ml 项目的成功。下表总结了 crisp-ml(q) 核心阶段和相应的任务： 致谢

CRISP-DM defines a reference framework for carrying out data mining projects and sets out activities to be performed to complete a product or service. The activities are organized in.

2022年8月2日 · 我们在本文中介绍了crisp-ml（q） 以及它如何侧重风险评估和质量保证。 我们先定义业务目标、收集和清理数据、构建模型、拿测试数据集验证模型，然后将其部署到生产环境中。

2023年12月3日 · In conclusion, the CRISP-ML(Q) is a comprehensive process that encompasses understanding business goals, preparing data, engineering models, ensuring quality, deploying solutions, and maintaining...

2022年10月3日 · 用于开发具有质量保证方法的机器学习应用程序的 跨行业标准过程 (CRISP-ML (Q) 是CRISP-DM的升级版本，以确保ML产品的质量。 CRISP-ML (Q)有六个单独的阶段： 这些阶段需要不断迭代和探索以构建更好的解决方案，即使在框架中有顺序，后期阶段的输出可以决定我们是否需要重新检查前一阶段。 在框架的每个阶段都引入了质量保证方法。 该方法具有要求和约束，如性能指标、数据质量要求和健壮性。 它有助于降低影响机器学习应用成功的风险，它 …

2022年12月18日 · After CRISP-DM was used to create Data Mining-based applications, some developers needed a new process standard specifically for ML, especially due to the rapid development of Deep Learning. So that raises CRISP-ML where ML is slightly different from DM. An integration with quality assurance (QA) results in the CRISP-ML (Q) development model.