CLAIMED，一个用于可信AI的可视化和可扩展组件库
CLAIMED, a visual and scalable component library for Trusted AI
摘要：
深度学习模型越来越受欢迎，但对可解释性、对抗性鲁棒性和公平性的限制通常是生产部署的主要问题。尽管开源生态系统在解决这些问题方面非常丰富，但开源缺乏完全集成的端到端系统。因此，我们在Kubernetes之上为生产级机器学习提供了一个完全开源、可重用的组件框架、可视化编辑器和执行引擎，这是IBM和巴塞尔大学医院的共同努力。它使用Kubeflow Pipelines，AI Explainability360工具包，AI Fairness360工具包和ElyraAI，Kubeflow，Kubernetes和Adversarial Robustness Toolkit。使用Elyra管道编辑器，可以使用一组jupyter笔记本可视化地开发AI管道。
英文摘要：
Deep Learning models are getting more and more popular but constraints on explainability, adversarial robustness and fairness are often major concerns for production deployment. Although the open source ecosystem is abundant on addressing those concerns, fully integrated, end to end systems are lacking in open source. Therefore we provide an entirely open source, reusable component framework, visual editor and execution engine for production grade machine learning on top of Kubernetes, a joint effort between IBM and the University Hospital Basel. It uses Kubeflow Pipelines, the AI Explainability360 toolkit, the AI Fairness360 toolkit and the Adversarial Robustness Toolkit on top of ElyraAI, Kubeflow, Kubernetes and JupyterLab. Using the Elyra pipeline editor, AI pipelines can be developed visually with a set of jupyter notebooks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
