Open Source · Nvidia · AI Agent · Hugging Face
NVIDIA Kumo Relational Outperforms Supervised Machine Learning on Relational Data, No Teaching Required
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NVIDIA Kumo Relational is a pre-trained relational foundation model that makes predictions directly on enterprise databases.
Key facts
- The paper also reports that on the SAP SALT enterprise benchmark, Kumo Relational surpasses large AutoGluon ensembles by 8% in mean reciprocal rank (MRR) and recent tabular foundation models
- Across 41 tasks on 15 databases, it beats supervised relational deep learning models and expert-engineered gradient-boosted tree pipelines, and on the SAP SALT enterprise benchmark it surpasses
- On SAP SALT, it beats XGBoost on engineered features by 7 MRR points out of the box and by 13 MRR points after fine-tuning
- To make a prediction, Kumo Relational has to do three things: (1) understand the values within each table, (2) relate records across tables, and (3) relate context examples with known targets
Summary
Across 41 tasks on 15 databases, it beats supervised relational deep learning models and expert-engineered gradient-boosted tree pipelines, and on the SAP SALT enterprise benchmark it surpasses AutoGluon ensembles by 8% in mean reciprocal rank (MRR). Foundation models have already rewritten the playbook for language and vision. The same shift is now underway for structured data. Better accuracy today: As drop-in replacements, these models already beat many production pipelines, especially XGBoost. Simplicity and agility: One pre-trained model replaces separate pipelines for individual tasks.