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A tabular foundation model that predicts without training. Send labeled and unlabeled rows, and get classification, regression, and forecasts back in seconds.
Classical ML trains a model per dataset. tabH2O is one model that reads your labeled rows as context and predicts the rest. The steps it removes are the steps projects stall on.
tabH2O is not a training engine. The Feature Store feeds it directly and predictions come out, with no model artifact and nothing for MLOps to deploy. Highlighted is tabH2O.
When the H2O AI Super Agent needs a forecast or a score inside a larger plan, tabH2O answers in seconds, with no experiment to schedule and no model to deploy. When the case needs a fully trained, documented model, the agent uses H2O Driverless AI instead.
Same platform either way: the same capabilities, the same governance, and the same audit trail. The choice stays open, so you can switch between them as the business changes, without rebuilding what runs on top.
The H2O AI Cloud for Government has achieved FedRAMP High Certification, delivering the highest standard of cloud security so federal agencies can harness predictive and generative AI without compromising data sovereignty or compliance.
tabH2O is H2O.ai's tabular foundation model. It is pretrained on millions of synthetic datasets and predicts on your table by reading labeled rows as context, with no training, tuning, or feature engineering. It returns predictions in under three seconds on 15,000 rows.
Free tier, no training. Or bring a forecasting problem and we will benchmark tabH2O against your current model, on your data.