- Sample use-case tutorials
- Validating historical features with Great Expectations
- Building streaming features
- Retrieval Augmented Generation (RAG) with Feast
- RAG Fine Tuning with Feast and Milvus
- MCP - AI Agent Example
- Feast-Powered AI Agent
- Demo Notebooks
- Feature Quality Monitoring Quickstart
- Running Feast with Snowflake/GCP/AWS
- Running Feast in production (e.g. on Kubernetes)
- Feast on Kubernetes
- Feast Production Deployment Topologies
- Online Server Performance Tuning
- Customizing Feast
- Adding or reusing tests
- Starting Feast servers in TLS(SSL) Mode
- Importing Features from dbt
- Entity Key Serialization (v2 to v3)
- Feature Quality Monitoring
- Codebase Structure
- Type System
- Data sources
- Offline stores
- Online stores
- Registries
- Providers
- Compute Engines
- Feature repository
- Kubernetes auth setup
- User token provisioning
- Feature servers
- [Beta] Web UI
- [Beta] On demand feature view
- [Alpha] Static Artifacts Loading
- [Alpha] Vector Database
- [Alpha] Data quality monitoring
- [Alpha] Streaming feature computation with Denormalized
- [Alpha] Feature View Versioning
- OpenLineage Integration
- MLflow Integration
- Feast CLI reference
- Python API reference
- Usage
- Contribution process
- Development guide
- Backwards Compatibility Policy
- Versioning policy
- Release process
- Feast 0.9 vs Feast 0.10+
- Architecture Decision Records
- ADR-0001: Feature Services
- ADR-0002: Component Refactor
- ADR-0003: On-Demand Transformations
- ADR-0004: Entity Join Key Mapping
- ADR-0005: Stream Transformations
- ADR-0006: Kubernetes Operator
- ADR-0007: Unified Feature Transformations
- ADR-0008: Feature View Versioning
- ADR-0009: Contribution and Extensibility
- ADR-0010: Vector Database Integration
- ADR-0011: Data Quality Monitoring
- ADR-0012: LabelView