NVIDIA Kumo Relational is a pre-trained relational foundation model that makes predictions directly on enterprise databases. Given a predictive query, it gathers up to 10,000 historical examples as context and predicts outcomes such as churn, demand, or fraud in a single forward pass. It needs no task-specific training, no feature engineering, and no flattening of tables. Kumo Relational is the first few-shot foundation model to outperform supervised machine learning on common relational benchmarks. 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). It scales to databases with 500B+ rows and works for humans and agents alike, through NVIDIA's open-source, GPU-native structured-data-models library or as an NVIDIA NIM.
NVIDIA Kumo Relational Outperforms Supervised Machine Learning on Relational Data, No Training Required
A Blog post by NVIDIA on Hugging Face
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Key points
- On RelBench V1 classification tasks, Kumo Relational outperforms gradient-boosted trees on expert-engineered features by nearly 4 AUROC points on average.
- Kumo Relational is already built to be called by agents, and its Predictive Query Language (PQL) gives them a simple interface for asking predictive questions.
- Today, we are introducing Kumo Relational (paper, documentation), a pre-trained foundation model that brings this paradigm to relational data.
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