In machine learning (ML), features are characteristic attributes of an instance or entity that you can use to train models or to make online predictions. Features are generated by transforming raw ML data into measurable and shareable attributes using feature engineering techniques, generally referred to as feature transformations .
Feature management refers to the process of creating, maintaining, sharing, and serving ML features stored in a centralized location or repository. Feature management makes it easier to reuse features to train and retrain models, reducing the life cycle of AI and ML deployments.
A product or service that includes feature management services to store, discover, share, and serve ML features is called a feature store . Gemini Enterprise Agent Platform incorporates the following feature store services:
This page provides an overview of the capabilities of Agent Platform Feature Store.
Agent Platform Feature Store
Agent Platform Feature Store offers a new approach to feature management by letting you maintain and serve your feature data from a BigQuery data source. In this approach, Vertex AI Feature Store acts as a metadata layer that provides online serving capabilities to your feature data source in BigQuery and lets you serve features online based on that data. You don't need to copy or import the data to a separate offline store in Gemini Enterprise Agent Platform.
Vertex AI Feature Store is integrated with Knowledge Catalog to track feature metadata. It also supports embeddings and lets you perform vector similarity searches for nearest neighbors.
Vertex AI Feature Store is optimized for ultra-low latency serving and lets you do the following:
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Store and maintain your offline feature data in BigQuery, taking advantage of the data management capabilities of BigQuery.
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Share and reuse features by adding them to the feature registry.
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Serve features for online predictions at low latencies using Bigtable online serving.
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Track feature metadata in Knowledge Catalog.
