Skip to content
Learni
View all tutorials
Machine Learning

How to Deploy a Feature Store with Feast in 2026

18 minADVANCED
MLOpsFeastFeature Store
Lire en français

Introduction

A Feature Store like Feast centralizes ML feature management, eliminates data leaks, and ensures consistency between training and inference. In 2026, teams require high-availability deployments with low-latency online stores and full traceability. This tutorial guides you through setting up an advanced Feast architecture, including registry, offline store, and CI/CD. You will learn to version features, materialize data, and expose real-time endpoints. Each step includes production-ready code.

Prerequisites

  • Python 3.10+
  • Docker and Kubernetes (EKS/GKE)
  • Advanced knowledge of PySpark and Redis
  • AWS/GCP account with IAM permissions
  • Feast 0.38+ installed

Initialize the Feast Project

terminal
mkdir feast-advanced && cd feast-advanced
feast init feature_repo --template local
cd feature_repo
pip install feast[redis,spark]==0.38.0

Initializes a structured Feast repository and installs the dependencies required for Redis and Spark. Pins release 0.38 to avoid version conflicts.

Feature Store Configuration

feature_store.yaml
project: advanced_feast
registry:
  registry_type: sql
  path: postgresql://user:pass@postgres:5432/feast
provider: local
online_store:
  type: redis
  connection_string: redis://redis:6379
offline_store:
  type: spark
  spark_conf:
    spark.master: "local[*]"
entity_key_serialization_version: 2

Configures a PostgreSQL registry for traceability, a Redis online store for <10ms latency, and Spark for offline computations. Serialization version 2 ensures future compatibility.

Define Entities and Features

features.py
from feast import Entity, FeatureView, FileSource, ValueType
from datetime import timedelta

user = Entity(name="user_id", join_keys=["user_id"], value_type=ValueType.INT64)

user_features_source = FileSource(
    path="s3://bucket/user_features.parquet",
    timestamp_column="event_timestamp",
)

user_features = FeatureView(
    name="user_features",
    entities=[user],
    ttl=timedelta(days=7),
    source=user_features_source,
    schema=[
        Field(name="age", dtype=ValueType.INT32),
        Field(name="income", dtype=ValueType.FLOAT),
    ],
    online=True,
)

Defines the user entity and feature view with TTL and explicit schema. The online=True field automatically enables Redis materialization.

Apply and Materialize

terminal
feast apply
feast materialize-incremental $(date -u +'%Y-%m-%dT%H:%M:%S')

Applies definitions to the registry and materializes recent features into Redis. Use materialize-incremental in production to control costs.

Query Features in Production

query.py
from feast import FeatureStore
import pandas as pd

store = FeatureStore(repo_path=".")
features = store.get_online_features(
    features=[
        "user_features:age",
        "user_features:income",
    ],
    entity_rows=[{"user_id": 12345}],
).to_df()

Retrieves real-time features from Redis with latency under 10 ms. Always handle cases where a feature is missing.

Kubernetes Deployment

feast-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: feast-serving
spec:
  replicas: 3
  template:
    spec:
      containers:
      - name: feast
        image: feastdev/feature-server:0.38.0
        env:
        - name: FEAST_REPO_PATH
          value: /app/feature_repo

Deploys the serving layer with high availability using 3 replicas. Mount the repo via ConfigMap or init container to enable zero-downtime updates.

Best Practices

  • Always version feature views with Git tags
  • Use short TTLs on volatile features
  • Monitor data freshness with Prometheus
  • Separate dev/staging/prod environments with distinct registries
  • Implement unit tests on Spark transformations

Common Mistakes to Avoid

  • Forgetting to materialize features before the first online call
  • Using feature names that are too long (>50 characters)
  • Ignoring entity key conflicts between views
  • Not configuring retention on the PostgreSQL registry

Go Further

Deepen your knowledge of feature governance and MLOps pipelines with our Learni training courses.