A large chemical company was seeking to use machine learning methods to develop a novel electronic device with specific properties. While the company had collected a relatively large volume of data that could be used to begin training their predictive models, the team quickly realized that they did not have the needed training sets internally to receive accurate predictions and results. Furthermore, the company did not have access to the diverse datasets needed to improve its model.
Turning to CAS for assistance, the company received several custom-curated datasets that were specially designed for their machine learning models. Over a period of six months, the datasets were further refined, which led to continuous improvements in their AI models. Read the case study to learn how custom data from CAS led to:
- Precise predictions for properties that were outside of the organization’s field of experience.
- The production of more custom datasets to further refine the research process.
- Completion of research projects in half the time when compared to similar efforts.