H2O Feature Store
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.
- H2O Feature Store is a centralized repository to store, update, retrieve, and share features across projects, with 40+ metadata attributes per feature.
- The offline store builds models; the online store serves inference in sub-millisecond time and feeds tabH2O directly.
- Per-feature drift alerts can trigger retraining; bias analysis and a full value-change log are built in.
- Co-developed with AT&T for a network carrying 465 petabytes of data traffic on an average day.
The seam between training and serving, closed.
Training/serving skew is where predictive projects die quietly. One feature definition, two stores, one truth.
- Offline storePoint-in-time correct features for training, feeding Driverless AI, H2O-3, and Hydrogen Torch, so the model learns on data it can trust.
- Online storeSub-millisecond serving for inference, feeding MLOps deployments and tabH2O directly, from the same definitions used in training.
- Automated feature engineeringEngineering, encoding, selection, and validation, automated. Recommendations surface features other projects have already built and proven.
- Drift alerts per featureDrift detected feature by feature, with alerts that can trigger retraining. Bias analysis, and a full log of every value change.
- GovernanceRBAC and IAM, versioning, lineage, and a searchable catalog: who built this feature, from what, and who else depends on it.
- Any data sourceSnowflake, Databricks, Spark, and Sparkling Water, with Python, Java, and Scala clients, Kubernetes-based and cloud agnostic.
From raw data to a feature the whole company reuses.
- 01RegisterDefine the feature set from Snowflake, Databricks, Spark, or files, with metadata and lineage captured as you go.
- 02MaterializeOffline store for training, online store for serving. Batch, streaming, or on demand, from one definition.
- 03ReuseSearch the catalog. Recommendations surface the features other teams have already built and validated.
- 04WatchDrift alerts per feature. Retrain automatically when a threshold trips, before the model quietly decays.
The first box in the predictive lane, and the source for tabH2O.
Everything in the predictive lane starts here. Deliberate boundary: the Feature Store does not feed H2O Cascade. Highlighted is the Feature Store.
What ships today
The features an agent trusts.
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.
You operate it, or H2O AI operates it for you.
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.
H2O AI Cloud for Government
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.
H2O Feature Store in production
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Questions people ask before they call us.
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.
Count how many times your teams have rebuilt the same feature.
We will register your top ten features, wire the online store to one production model, and switch on drift alerts.