论文标题
验证和启用工业AI的机器学习
Validate and Enable Machine Learning in Industrial AI
论文作者
论文摘要
工业人工智能(工业AI)是一个新兴的概念,是指人工智能在行业中的应用。工业AI承诺将更加有效的未来工业控制系统。但是,制造商和解决方案合作伙伴需要了解如何将AI模型实施和集成到现有的工业控制系统中。训练有素的机器学习(ML)模型为工业控制优化提供了许多好处和机会;但是,下等工业人工智能设计和集成限制了ML模型的能力。为了更好地了解如何将受过训练的ML模型开发到传统的工业控制系统中,测试已部署的AI控制系统,并最终胜过传统系统,制造商及其AI解决方案合作伙伴需要解决许多挑战。在论文中探讨了我们在部署工业AI时遇到的六个主要挑战,这是我们遇到的真正问题。 Petuum最佳系统被用作展示制作和测试AI模型的挑战,更重要的是,如何应对工业AI系统中的此类挑战。
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.