Showa Denko has introduced MLOps (Machine Learning Operations) for efficient management of machine learning models deployed into Artificial Intelligence (AI) systems for materials design ahead of its competitors.
Machine learning models can predict material properties based on formulations and manufacturing-process conditions of materials.
This time, we automated input of the latest data into computers that develop machine learning models and data processing in those computers.
This automation has reduced the time required to build and operate machine learning models from five days to one day per month. In addition, the introduction of MLOps enabled us to accelerate materials development by predicting material properties based on the latest data.
SDK utilizes AI systems for efficient materials development, such as exploring the optimal material formulation. Machine learning models deployed into the AI systems predict material properties from formulations or suggest formulations that improve material properties.
The machine learning process for managing the AI systems includes inputting the latest data, data processing, and continuous training of machine learning models.
Previously, data scientists had to input and process the latest data for themselves. These steps accounted for about 80% of the time required for the entire machine learning process. In addition, machine learning models deployed into the AI systems are built specifically for each material. Therefore, before Showa Denko introduced MLOps, the development of machine learning models required a lot of time and effort due to the necessary work specialized for each material.
Aiming to address these issues caused by applying AI systems to the development of numerous materials in the Company and operating machine learning models efficiently, we have installed programs to automate the input of the latest data and data processing into our AI systems.
Moreover, we have introduced technologies that enable data scientists responsible for building machine learning models and software engineers responsible for building AI systems to develop systems collaboratively even if there are differences in operating systems and programming languages they use.
By introducing MLOps ahead of our competitors to manage machine learning models efficiently, we could reduce the time required to develop machine learning models and their operation, improve prediction accuracy, and stably operate dozens of AI systems. As a result, now we can propose ideal materials to our customers promptly.
The Showa Denko Group will apply the fruits of basic research in AI and computational science to materials development and quickly provide solutions that solve our customers’ problems, thereby contributing to the development of a sustainable society.