Implementation of an Automated Learning System for Non-experts

TL;DR

This paper presents YMIR, an automated machine learning system with a graphical interface designed for non-experts, enabling easy data labeling, model training, and evaluation without AI expertise.

Contribution

The paper details the engineering implementation of YMIR, including open model training, resource management, integrated labeling, and human-computer interaction, facilitating accessible AI development.

Findings

Successful deployment of YMIR for model training

User-friendly interface enables non-experts to operate AI systems

Open-source code available for community use

Abstract

Automated machine learning systems for non-experts could be critical for industries to adopt artificial intelligence to their own applications. This paper detailed the engineering system implementation of an automated machine learning system called YMIR, which completely relies on graphical interface to interact with users. After importing training/validation data into the system, a user without AI knowledge can label the data, train models, perform data mining and evaluation by simply clicking buttons. The paper described: 1) Open implementation of model training and inference through docker containers. 2) Implementation of task and resource management. 3) Integration of Labeling software. 4) Implementation of HCI (Human Computer Interaction) with a rebuilt collaborative development paradigm. We also provide subsequent case study on training models with the system. We hope this paper can facilitate the prosperity of our automated machine learning community from industry application perspective. The code of the system has already been released to GitHub (https://github.com/industryessentials/ymir).