H2O AI Feature Store

Automatic Feature Recommendations

Automatically improve the features in your feature store and also generate new features with our state-of-the-art automated feature engineering. Data scientists can select the feature sets that they are looking to update and improve, and simply request feature recommendations. The H2O AI Feature Store will automatically recommend new features and feature updates that could improve AI model performance. Data Scientists can review the proposed updated features and accept or discard them, retaining complete control. Users can set up feature recommendations to run automatically or on-demand.

Automatic Feature Drift

Automatically checks both individual features and feature sets for drift over time and alerts users. Alerts can be used to trigger retraining or refitting to keep models accurate.

Automatic Bias Identification

Automatically detect bias in your features. Data Scientists can simply select the set of features they’d like to analyze for bias, and the H2O AI Feature Store will analyze and report if bias was detected. This capability helps data scientists monitor features on an ongoing basis to continually remove bias. With our automatic Bias Identification feature, data scientists have complete control to review and take action on features that may create bias.

Feature Rank

Automatically scores features to indicate popularity or value across different use-cases.

Detailed Cataloging

Add over 40 metadata attributes, such as Description, Data Sources, and Data Sensitivity Categories. Additionally, metadata tags can be added to further improve the feature discoverability and exploration. The complete list of attributes is located in our H2O AI Feature Store documentation.

Feature Search

Use natural language search to find the best features using simple or advanced queries.

Automated Feature Metadata

Explore and find features based on automatically generated feature statistics like mean, standard deviation, frequencies etc.

Integration with Popular Feature Engineering Pipelines

Integrated with Snowflake, Databricks, H2O Sparkling Water, Apache Spark, Python, Java, and Scala feature engineering pipelines. Other data engineering pipelines can write features and metadata attributes to the H2O AI Feature Store via the REST API.

Integration with H2O AI Cloud

Integrated within H2O.ai’s state-of-the-art cloud platform, H2O AI Cloud. H2O AI Cloud supports data exploration, automated feature engineering, model building with H2O3 and H2O Driverless AI, model monitoring and operations, and a broad set of pre-built AI applications.

Real-time Model Scoring

Features that are needed to support real-time applications are stored in-memory and served with sub-millisecond latency.

Scalable

Scale to handle nearly any number of real-time or batch reads / writes. Kubernetes is utilized to make scaling easier to manage for customers.

Single Sign On

Integrate existing identity and access management tools.

Feature Permissions

Grant role-based and individual permissions to specific projects and features.

Feature Versioning

Store versions of features and their associated metadata to comply with regulations, backtest models, and learn from past features.

ML Time Travel

Automatically log changes to all feature values, allowing data scientists and ML governance organizations to understand how past models would have scored records at any given time.

Automated Feature Insights

Automatically compute feature statistics like mean, median, standard deviation, frequencies etc. for quick insights on features.