  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.
