MLExchange: A web-based platform enabling exchangeable machine learning workflows for scientific studies
This addresses the need for user-friendly ML tools in scientific communities, though it is incremental as it builds on existing containerization and web technologies.
The authors tackled the problem of making machine learning accessible to scientists without deep ML expertise by developing MLExchange, a web-based platform that enables exchangeable ML workflows and data management, deployable across devices from personal laptops to high-performance clusters.
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. 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 personal device (laptop, smart phone, etc.) 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.