Developing a Best Practices Training Program in Cyberinfrastructure-Enabled Machine Learning Research
Developing a Best Practices Training Program in Cyberinfrastructure-Enabled Machine Learning Research
复制标题
制定网络基础设施支持的机器学习研究最佳实践培训计划
DOI：
10.1145/3569951.3597543
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Sinkovits, Robert S.
中科院分区：
文献类型：
--
作者：
Thomas, Mary P.;Goetz, Andreas W.;Kandes, Martin C.;Nguyen, Mai;Rodriguez, Paul;Rose, Peter W.;Sinkovits, Robert S.
今天，机器学习正在科学、工程和医学的各个领域得到应用。尽管有许多关于机器学习的教育资源，但它们关注的是在适度规模上执行工作流。我们的培训计划，网络基础设施支持的机器学习（CIML），专注于大规模机器学习工作流程的核心能力，并集成了高性能计算，数据管理，高级网络基础设施，可重复计算，可扩展机器学习和深度学习等主题。CIML项目每年举办一次研讨会，汇集了来自各个领域的研究人员和从业人员，培训计划侧重于向参与者大规模教授高性能计算（HPC）和机器学习的基础知识。采用“可查找、可访问、可互操作和可重用”（FAIR）的做法，所有CIML培训材料都通过GitHub免费在线提供。我们描述了CIML项目及其培训计划，报告了迄今为止的影响，并讨论了我们对该项目的未来计划。
Today, machine learning is being deployed for use in practice across all fields of science, engineering, and medicine. Although there are many educational resources on machine learning, they focus on executing workflows at modest scale. Our training program, Cyberinfrastructure-Enabled Machine Learning (CIML), focuses on the core competencies for at-scale ML workflows, and integrates topics from high-performance computing, data management, advanced cyberinfrastructure, reproducible computing, calable machine learning, and deep learning. The CIML project hosts an annual workshop that brings together researchers and practitioners from all fields, where the training program focuses on teaching participants the basics of high-performance computing (HPC) and ML at scale. Adopting "Findable, Accessible, Interoperable, and Reusable" (FAIR) practices, all CIML training material is made freely available online via GitHub. We describe the CIML project and its training program, report on its impact to date, and discuss our future plans for the project.
通过先进的网络基础设施实现科学民主化
影响因子：
2.2
作者：
M. Parashar
通讯作者：
M. Parashar