Feature Store

13 items tagged with "feature-store"

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Best Practices1

Feature Store Best Practices

A centralized system for defining, storing, and serving machine learning features consistently for training and inference, avoiding skew and duplicated work.

Blueprints2

Blueprint

Batch ML Scoring to Real-Time Model Serving Blueprint

Move from nightly batch ML scoring to low-latency online model serving with a model server, feature lookups, and autoscaling.

Blueprint

Classic ML to Feature Store and Serving Blueprint

Migrate ad-hoc feature engineering for classic ML models to a central feature store with consistent offline training and online serving.

Reference Architectures6

Reference Architecture

Feature Store Platform

A feature store on GCP providing consistent online and offline ML features with point-in-time correctness via Feast.

Reference Architecture

Real-Time Model Serving on GCP

A reference design for low-latency online inference on GCP using Vertex AI endpoints, autoscaling, and a feature lookup path for sub-100ms predictions.

Reference Architecture

End-to-End MLOps Platform on Kubernetes

A reference design for a portable MLOps platform on Kubernetes covering pipelines, experiment tracking, a model registry, serving, and monitoring.

Reference Architecture

Feature Store and Online Serving on AWS

A reference design for a dual offline/online feature store on AWS that keeps training and serving features consistent and serves them at low latency.

Reference Architecture

Recommendation System on AWS

A reference design for a two-stage recommender on AWS combining candidate retrieval and ranking, with streaming feedback and real-time serving.

Reference Architecture

Streaming ML Feature Pipeline on GCP

A reference design for a real-time feature engineering pipeline on GCP that computes streaming aggregates and serves them to online models consistently.

Playbooks1

Playbook

Feature Store Program Playbook

Build a feature store to unify ML feature engineering, ensure online and offline consistency, and enable feature reuse across teams.

Stacks2

Stack

Feast Feature Store Stack

Feature store pattern using Feast to define, materialize, and serve consistent ML features from an offline warehouse and a low-latency online store.

Stack

Tecton Feature Store

A production feature platform stack using Tecton to define, compute, and serve consistent ML features for training and real-time inference.

Comparisons1

Comparison

Feature Store: Build vs Buy

Teams can build a custom feature store or adopt a managed or open-source one. The choice trades control and fit against time-to-value and maintenance burden.