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A centralized feature store that defines a feature once and serves it from two stores, offline for training and online for sub-millisecond inference, with no training-serving skew.
Training/serving skew is where predictive projects die quietly. One feature definition, two stores, one truth.
Everything in the predictive lane starts here. Deliberate boundary: the Feature Store does not feed H2O Cascade. Highlighted is the Feature Store.
When the Data Science Agent builds a model, it starts from features that already exist, already validated, already monitored. When the Super Agent calls tabH2O for a score, the online store supplies the rows. The Feature Store is how agents avoid inventing data.
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.
A feature store is a centralized repository for the engineered inputs to machine learning models, so features are defined once, versioned, shared across projects, and served consistently for training and for real-time inference. H2O Feature Store provides both an offline store and an online store.
We will register your top ten features, wire the online store to one production model, and switch on drift alerts.
