MLExchange: A web-based platform enabling exchangeable machine learning workflows for scientific studies

TL;DR

MLExchange is a web-based platform designed to make machine learning workflows accessible and exchangeable for scientists without deep ML expertise, supporting flexible deployment from personal devices to HPC clusters.

Contribution

It introduces a modular, containerized platform that simplifies ML workflow management and enables easy deployment across various computational environments.

Findings

Supports exchange of ML algorithms, workflows, and data via web interface

Flexible deployment from personal devices to HPC clusters

Facilitates scientific discovery with minimal ML background required

Abstract

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.