Machine learning (ML) algorithms are showing a growing trend in helping the scientific communities across different disciplines and institutions to address large and diverse data problems. However, many available ML tools are programmatically demanding and computationally costly. The MLExchange project aims to build a collaborative platform equipped with enabling tools that allow scientists and facility users who do not have a profound ML background to use ML and computational resources in scientific discovery. At the high level, we are targeting a full user experience where managing and exchanging ML algorithms, workflows, and data are readily available through web applications. So far, we have built four major components, i.e, the central job manager, the centralized content registry, user portal, and search engine, and successfully deployed these components on a testing server. Since each component is an independent container, the whole platform or its individual service(s) can be easily deployed at servers of different scales, ranging from a laptop (usually a single user) to high performance clusters (HPC) accessed (simultaneously) by many users. Thus, MLExchange renders flexible using scenarios -- users could either access the services and resources from a remote server or run the whole platform or its individual service(s) within their local network.

MLExchange项目旨在通过构建协作平台来帮助不具备深度机器学习背景的科学家和实验用户使用机器学习和计算资源，旨在为科学领域中不同领域和机构的科学社区解决大规模和多样化的数据问题。

MLExchange：一个基于Web的平台，为科学研究提供可交换的机器学习工作流程