It’s not just about putting in a food order. It’s about providing an optimal experience for our customers. It’s really, really fast. Plus it’s a lot cheaper.
Daniel GalinkinHead of ML Engineering
Redis Feature Form
Machine learning thrives on real-time data, but creating real-time pipelines can seem impossible. Not with Feature Form. Simply define your features as code, and Feature Form will turn them into prod-grade data pipelines for model training and inference.
Operationalize real-time data for machine learning
Feature Form orchestrates between your offline data systems and real-time serving. It turns your feature definitions into computation across Snowflake, Databricks, and more, and keeps your Redis online store up to date for low-latency feature retrieval.
- Real-time feature pipelines made easy
- Works on your existing data stack
- Define features once and use them for training and inference
Built for enterprise AI teams
Keep teams from reinventing features, understand which models are using which features, and learn how features are evolving over time.
- Workspace-scoped auth and permissions
- Isolated data providers and configs
- Independent observability per workspace
Get finely-tuned feature control
Plan and validate changes before they hit production with full visibility into how updates affect downstream pipelines and models.
- Plan changes with ff apply --plan
- Run impact analyses across pipelines
- Split materializations into discrete jobs
- Trace execution with OpenTelemetry
Keep data secure at any scale
Maintain full access control, auditability, and stay compliant without compromising speed. Security is part of the system.
- Workspace-scoped RBAC
- API key pairs and model roles
- Audit logs for every change
- Secret providers, mTLS, encrypted transport
We also saw a 38% decrease in Redis latencies, helping to improve the runtime performance of serving models.
Arbaz KhanStaff Software Engineer
Redis Feature Form
Workspaces & multi-tenancy
Run multiple ML teams on one platform with workspace isolation, scoped access, and independent environments.
Atomic DAGs & version control
Plan, apply, and roll back changes at the graph level so updates are consistent, traceable, and never partially applied.Scale with Apache Iceberg for indexing, time travel, and high-performance access to large datasets.
One platform, full feature lifecycles
Define features once, orchestrate pipelines across your data stack, and serve in real time—without stitching together scripts and systems.
Learn about tracking
Ensure models see the same feature definitions in training and production, eliminating drift and preventing silent failures
Redis as an online store
Open architecture, no lock-in
Integrate with Snowflake, Databricks, Spark, Postgres, and more while Redis handles real-time serving.Use Feast as your offline feature store and Redis for real-time online serving.
Sub-ms feature serving
Serve features instantly at scale for fraud detection, recommendations, real-time decisioning, and more.
Built for high availability
Built-in replication and failover keep features available when your models need them.
Keep features fresh in real time
Continuously update feature values so models always use the latest data.
Get started
Meet with a Redis expert and build your feature store today.