Agentic AutoML on Relational Databases

Most structured data lives in relational databases, but AutoML still assumes a single flat table — so humans spend their effort flattening. This project surveys AutoML and relational deep learning, sketches a formalism for agents that explore schemas, enrich features and search models directly on the database, and asks whether copy-on-write database clones are the right substrate for the many experiment versions such agents create.

Ouael Ben Amara

Project lead · university of Michigan

Team

Ouael Ben Amara

Project lead

0

contributions

LEADsince Jul 2026

CRediT roles

ConceptualizationSupervisionProject administration

Provisional author line

Ouael Ben Amara

Authorship here follows the CRediT taxonomy. Every contributor can see who is credited for what, and in which order, before anything is submitted anywhere.