Topic

Relational Data

1 post about Relational Data. Every summary links to the original.

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Hugging Face
NVIDIA Kumo Relational Outperforms Supervised Machine Learning on Relational Data, No Training Required
NVIDIA Kumo Relational is a pre-trained relational foundation model that makes predictions directly on enterprise databases without requiring task-specific training, feature engineering, or table flattening. Given a predictive query, it gathers up to 10,000 historical examples as context and produces outcomes such as churn or demand in a single forward pass. Across 41 tasks on 15 databases, it outperforms supervised relational deep learning models and gradient-boosted tree pipelines; on the SAP SALT benchmark it surpasses AutoGluon ensembles by 8% in mean reciprocal rank. The model scales to databases with over 500 billion rows and is available through NVIDIA’s open-source structured-data-models library or as an NVIDIA NIM. It reasons within tables using column and row attention and across tables using graph attention over foreign keys, avoiding quadratic cost.

The best engineering reads, once a week.

Topic

1 post about Relational Data. Every summary links to the original.

Paid picks we use ourselves. Links carry a referral code — a subscription through one earns us a commission at no extra cost to you.

Newest first

Hugging Face
NVIDIA Kumo Relational Outperforms Supervised Machine Learning on Relational Data, No Training Required
NVIDIA Kumo Relational is a pre-trained relational foundation model that makes predictions directly on enterprise databases without requiring task-specific training, feature engineering, or table flattening. Given a predictive query, it gathers up to 10,000 historical examples as context and produces outcomes such as churn or demand in a single forward pass. Across 41 tasks on 15 databases, it outperforms supervised relational deep learning models and gradient-boosted tree pipelines; on the SAP SALT benchmark it surpasses AutoGluon ensembles by 8% in mean reciprocal rank. The model scales to databases with over 500 billion rows and is available through NVIDIA’s open-source structured-data-models library or as an NVIDIA NIM. It reasons within tables using column and row attention and across tables using graph attention over foreign keys, avoiding quadratic cost.

The best engineering reads, once a week.