Overview

The H2O AI Feature Store is a centralized platform designed to streamline the management and reuse of machine learning features across various projects. Developed in collaboration with AT&T, it facilitates the storage, updating, and sharing of features essential for building AI models. By organizing and governing features, the platform enhances collaboration among data scientists, engineers, and business analysts, accelerating the delivery of impactful AI outcomes.

Features and Capabilities

• Centralized Feature Repository: Provides a unified storage system for features, ensuring consistency and accessibility across teams.
• Integration with Data Pipelines: Supports integration with popular data engineering tools like Databricks, Snowflake, and Apache Spark, enabling seamless feature ingestion.
• Metadata Management: Allows the addition of over 40 metadata attributes, including descriptions and data sources, to enhance feature discoverability.
• Real-time and Batch Processing: Offers both online and offline stores to handle real-time scoring and batch processing needs.
• Automated Feature Recommendations: Utilizes AI to suggest new features or feature combinations that could improve model performance.
• Bias Detection: Automatically identifies and alerts users to potential biases in features, promoting fairness in AI models.
• Feature Drift Monitoring: Monitors features for drift over time and provides alerts to maintain model accuracy.
• Version Control: Maintains version history of features, enabling rollback to previous versions if necessary.
• Access Control: Supports role-based access to ensure secure and appropriate use of features.
• Collaboration Tools: Facilitates collaboration among data scientists, engineers, and business analysts through shared access to features.