CC-TAIN01 Recently, Feifei MAO, Director of Digital transformation of Schneider Electric Global Supply Chain China, was invited by Amazon Cloud Technology to discuss the challenges and opportunities faced by manufacturing enterprises in the deepening stage of digital transformation and innovation, the trend insight of AI technology, especially generative AI application in the manufacturing industry, and shared the progress made by Schneider Electric in related fields.
MAO Feifei pointed out that the industry’s well-known “20 to 80 law” also exists in the manufacturing industry AI application. “Two” refers to the number of industrial product models that can be scaled up and replicated. For such standardized AI application scenarios, future “out of the box” or minor retraining fine-tuning can be achieved through a single build of the model. For specific fragmented and long-tail scenarios, which account for about “eight” (80%), customized models need to be developed. Schneider Electric is working with cloud technology providers, including Amazon Cloud Technology, to develop an end-to-end training platform for model training, deployment, iteration and lifecycle management. In addition, in terms of the application of the current hot generative AI large language model, MAO FCC-TAIN01 eifei believes that in the short term, the large model will not swallow the small model, and the two are complementary. In the short term, the small model will evolve with the big model and survive for a long time. On the other hand, the large model can also assist the training and improve the accuracy of the small model.
As a global expert in digital transformation in energy management and automation, Schneider Electric is committed to continuing to deepen the field of AI, developing customized AI solutions to empower manufacturing applications. In June 2023, Schneider Electric released EcoStruxure AI engine, an enterprise-level one-stop, scenario-oriented and open AI model production and operation and maintenance platform, integrating expertise in the field of energy management and automation into AI models, providing low code and even zero code AI applications for business experts, data analysts and other users. Help them better solve the business problems they face in industrial scenarios, improve efficiency and innovate. The successful application of the EcoStruxure AI engine within Schneider Electric’s China supply chain uses a wide range of technologies and services provided by service providers such as Amazon Cloud Technologies. Amazon Cloud’s database and computing services, as well as Amazon SageMaker machine learning services, can provide complete technical support for EcoStruxure AI engine, improve the efficiency of solution implementation, and effectively save costs.
First, in terms of data storage, Amazon Cloud Technology can provide managed Relational DatCC-TAIN01 abase Service (Amazon RDS) for Schneider Electric EcoStruxure AI engine, It helps customers set up, operate, and scale relational databases in the cloud to easily complete time-consuming database administration tasks. In addition, the Elastic scalable, secure, and highly available Cloud server Amazon Elastic Compute Cloud (Amazon EC2) provides a variety of computing instance types in the cloud, which can provide flexible computing resources according to different needs, so as to meet the needs of different AI application scenarios of Schneider Electric. Amazon Cloud Technology can also provide EcoStruxure AI engine with Amazon Elastic Container Service (Amazon ECS) fully managed Docker container orchestration service, through this highly secure, reliable and scalable container operation. Easy deployment and management of machine learning models in production line edge device containers. Storing, managing, and deploying Docker Container image service Amazon Elastic Container Registry (Amazon ECR) allows Schneider Electric to easily manage and deploy containerized AI applications using a secure and efficient container image storage and management service.
