Recommendation System in Industrial Context
In recent years, there has been a rapid advancement in the field of machine learning, especially deep learning. These advancements have resulted in the emergence of technologies where artificial intelligence assists human activities and sometimes even performs rudimentary and repetitive human tasks. Most of these developments are concentrated in day-to-day human activities. Be it the use of recommendation systems to assist humans’ in picking the right movies or recommending items they might like or translating speech and texts from one language to another. Nevertheless, these advancements can also potentially extend to an industrial context such as the production industry or construction industry. These industries mainly rely on the experience of domain experts to make complex decisions in various situations by analysing decisive parameters. In such cases, artificial intelligence can assist domain experts in making decisions by readily providing data-driven recommendations. These recommendations can then be reviewed by domain experts to make the final decision. In this talk, I would like to present such a use case where a custom-developed recommendation system using language models and vector databases assists domain experts in the construction industry in decision making.
Manish Bhandari is a Data Scientist at XITASO GmbH developing a recommendation system using language models and vector databases to assist domain experts in decision making. He completed his Master’s degree in Digital Engineering at Otto von Guericke University in Magdeburg, and his Bachelor’s degree in Mechanical Engineering at Vidyavardhaka college of Engineering in Mysuru, Karnataka, India.