In-database semantic analytics using large database models
Use a large database model trained on selected columns from relational-database tables or views to answer semantic questions directly through SQL, without extracting the data into a separate analytics platform or sending it to an external AI model.
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From IBM Technology — What Are Large Database Models? AI for SQL Data at 01:33
Problem: Rigid SQL filters require data scientists to guess which fields define similarity, while traditional analysis requires extracting records, moving them into an analytics platform, and reporting the results back. This adds time, IT expense, and security and tracking concerns.
For: Enterprise teams in insurance, fraud detection, contract management, retail, and other organizations whose operational data is stored in relational databases.
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