Deployment and MLOps · MLD-0424
Feature store
A system for managing, discovering, and serving reusable features with consistent definitions.
Original reference page · detailed expansion pending. Original definitions, examples, notes and citations are preserved below. New examples are illustrative, not benchmark results.
Original example
Share validated features across models and environments.
Original caveat
A feature store does not automatically eliminate leakage.
Context and aliases
Level: Intermediate. Stages: Deployment · Monitoring.
Tags: ML · DL · LLM · SLM · MLOps.
Aliases: None recorded.
Related concepts
Concepts linking here
Technology connections
- Apache Airflow — Workflow orchestration platform centered on scheduled DAGs.
- Apache Iceberg — Open table format for analytical data lakes.
- Apache Kafka — Distributed event-streaming platform.
- Apache Parquet — Columnar file format with encoding and compression.
- Apache Spark — Distributed engine for data processing and analytics.
- Dagster — Asset-oriented data orchestration platform.
- Dask — Parallel computing library with array, DataFrame and task APIs.
- Feast — Feature-store framework.
- Prefect — Pythonic workflow orchestration and execution tooling.
- Ray — Distributed Python runtime for tasks and actors.
Original references
- Transformers glossary ↗ · Consulted
- Google Cloud MLOps architecture ↗ · Reference link
- scikit-learn User Guide ↗ · Consulted
- Original reference URL ↗