NVIDIA opens Kumo Relational, which predicts churn and fraud directly on relational databases without training and beats supervised pipelines on 41 tasks across 15 databases
Structured data is getting its own foundation models. How it predicts, how it scores, and how it's licensed.
// Key points
- A pretrained relational foundation model: for a predictive query it gathers up to 10,000 historical examples as context and predicts in a single forward pass, with no task-specific training, feature engineering or flattening of tables.
- Across 41 tasks on 15 databases it beats supervised relational deep learning models and expert-engineered gradient-boosted tree pipelines; on the SAP SALT enterprise benchmark it beats AutoGluon ensembles by 8% in MRR.
- It scales to databases with 500B+ rows and runs through NVIDIA's open-source structured-data-models library or as a NIM; the library downloads the weights from the Hugging Face Hub on first use.
- The code is under Apache 2.0 and the model weights under the OpenMDW License Agreement 1.1.
Builder's takeWhat small teams lack is the people to build a feature and training pipeline for every prediction task. For questions like subscription churn in AI Interview or storage growth in AI Cloud Drive, I'd run it on my own database as a baseline and compare it with rules or a simple model before investing more. The results are the vendor's own, so trust your data over them, and have someone read the weights license first.
// Background · from #Open models
Full timeline →- Oct 22 IBM releases Granite Speech 5.0 2B under Apache 2.0, with a 5.06% average English WER and keyword biasing for up to 2,000 terms
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