Description
Databricks Feature Store acts as a centralized registry for features used in AI and machine learning models. By registering feature tables and models within Unity Catalog, users gain integrated governance, lineage tracking, point-in-time joins, and the ability to share and discover features across workspaces. This platform streamlines the entire model training workflow, from data ingestion and feature table creation to model training and batch inference, all within a unified environment.
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Key capabilities include data pipelines for ingesting raw data, creating feature tables, training models, and performing batch inference. It also provides one-click access to model and feature serving endpoints with millisecond latency, alongside data and model monitoring. When features from Databricks Feature Store are used for training, the model automatically tracks lineage. At inference time, the model retrieves the latest feature values. The Feature Store also handles on-demand feature computation for real-time applications, eliminating training-serving skew and simplifying client-side code by managing all feature lookups and computations.
For workspaces enabled with Unity Catalog, Databricks Feature Store offers a robust set of tools for developing, discovering, sharing, and serving features. Users can create and manage feature tables, define declarative features with time-windowed aggregations, and materialize them for offline training or online serving. Feature exploration is facilitated through Catalog Explorer and the Features UI, with support for tags to categorize and manage feature tables. The platform ensures point-in-time correctness for training datasets, reflecting feature values at the time of label observation. Online Feature Stores are available for serving feature data to online applications and real-time ML models, powered by Databricks Lakebase. Model serving includes automatic feature lookup from online stores, and feature serving endpoints can be exposed for external applications. On-demand feature computation at inference time further enhances real-time capabilities. Governance and lineage are managed through Unity Catalog, controlling access and providing visibility into feature table, model, and function lineage.
- CategoryMachine Learning Platforms
- DeploymentCloud
- PlatformsWeb
Databricks Feature Store's Core Features
Centralized feature registry
Unity Catalog integration
Built-in governance
Feature lineage tracking
Point-in-time joins
Cross-workspace feature sharing
Feature discovery
Unified model training workflow
Data pipelines for feature engineering
Batch inference capabilities
Online feature serving endpoints
Real-time model serving
On-demand feature computation
Eliminates training-serving skew
Simplifies client-side code
How to use Databricks Feature Store?
Develop features: Create and manage feature tables using declarative APIs.
Materialize features: Prepare declarative features for offline training or online serving.
Discover features: Explore and manage feature tables via Catalog Explorer and the Features UI.
Train models: Utilize feature tables to train machine learning models.
Serve features: Deploy online feature stores for real-time applications.
Monitor data and models: Track performance and detect drift.
Databricks Feature Store's Use Cases
- ML Model Training
- Real-time Inference
- Feature Management
- Data Lineage Tracking
- Collaborative Feature Engineering
- Batch Scoring
- Retrieval Augmented Generation
