arXiv:2602.13697v2 Announce Type: replace-cross Abstract: Relational databases (RDBs) contain vast amounts of heterogeneous tabular information that can be exploited for predictive modeling purposes. But since the space of potential targets is vast across enterprise settings, how can we avoid retraining a new model each time we wish to predict a new quantity of interest? Foundation models based on in-context learning (ICL) offer a convenient option, but so far are largely restricted to single-table operability. In generalizing to multiple interrelated tables, it is essential to compress variab
The paper addresses a current challenge in AI, specifically how to leverage foundation models more effectively with complex, real-world enterprise data structures like relational databases, at a time when 'foundation models' and 'AI agents' are converging concepts.
It proposes a method to significantly reduce the computational and developmental overhead of deploying AI for predictive modeling across diverse enterprise needs, accelerating the practical application of advanced AI.
This research suggests a pathway to more flexible and less resource-intensive deployment of in-context learning foundation models across multi-table relational databases, potentially democratizing access to powerful AI predictive capabilities.
- · Enterprise AI end-users
- · AI platform providers
- · Data scientists
- · Cloud computing providers
- · Traditional bespoke ML model trainers
Enterprises can rapidly deploy predictive AI leveraging their existing relational database structures without extensive model retraining.
This could lead to a faster adoption of AI agents that can query and derive insights from complex enterprise data autonomously.
Increased efficiency in AI deployment might accelerate the 'AI agents' narrative, driving further investment and innovation in autonomous systems for business operations.
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Read at arXiv cs.LG