Validate and Enable Machine Learning in Industrial AI

TL;DR

This paper discusses the challenges of developing, integrating, and testing machine learning models in industrial control systems, using the Petuum Optimum system as an example to illustrate solutions.

Contribution

It identifies six key challenges in deploying Industrial AI and demonstrates how to address them through practical system implementation and testing.

Findings

Identified six top challenges in Industrial AI deployment.

Showcased solutions using the Petuum Optimum system.

Improved understanding of AI integration in industrial control.

Abstract

Industrial Artificial Intelligence (Industrial AI) is an emerging concept which refers to the application of artificial intelligence to industry. Industrial AI promises more efficient future industrial control systems. However, manufacturers and solution partners need to understand how to implement and integrate an AI model into the existing industrial control system. A well-trained machine learning (ML) model provides many benefits and opportunities for industrial control optimization; however, an inferior Industrial AI design and integration limits the capability of ML models. To better understand how to develop and integrate trained ML models into the traditional industrial control system, test the deployed AI control system, and ultimately outperform traditional systems, manufacturers and their AI solution partners need to address a number of challenges. Six top challenges, which were real problems we ran into when deploying Industrial AI, are explored in the paper. The Petuum Optimum system is used as an example to showcase the challenges in making and testing AI models, and more importantly, how to address such challenges in an Industrial AI system.