Complete Vertex AI Feature Store Tutorial: Centralized Feature Management

Vertex AI Feature Store is a centralized repository for organizing, storing, and serving ML features. It enables feature reuse, reduces training-serving skew, and provides consistent feature access across teams.

Why Feature Store?

• Centralized features: Single source of truth
• Feature reuse: Share features across models
• Low latency serving: Fast online feature retrieval
• Consistency: Same features for training and serving
• Time travel: Point-in-time feature lookups

Prerequisites

pip install google-cloud-aiplatform

gcloud auth login
gcloud config set project your-project-id

Setup

1. Initialize Vertex AI

from google.cloud import aiplatform

aiplatform.init(project="your-project-id", location="us-central1")

2. Create Feature Store

# Create feature store
featurestore = aiplatform.Featurestore.create(

    featurestoreid="myfeaturestore",
    onlinestorefixednodecount=1
)

print(f"Feature store created: {featurestore.resourcename}")

Entity Types

1. Create Entity Type

# Create customer entity type
customerentity = featurestore.createentitytype(
    entitytypeid="customer",
    description="Customer entity for churn prediction"
)

Create product entity type
productentity = featurestore.createentitytype(
    entitytypeid="product",
    description="Product entity for recommendation"
)

2. List Entity Types

entitytypes = featurestore.listentitytypes()
for et in entitytypes:

    print(f"{et.entitytypeid}: {et.description}")

# Create features for customer entity
customerentity.createfeature(

    featureid="age",
    valuetype="INT64",

    description="Customer age"
)

customerentity.createfeature(

    featureid="tenuremonths",

    valuetype="INT64",
    description="Months as customer"
)

customerentity.createfeature(
    featureid="monthlycharges",
    valuetype="DOUBLE",

    description="Monthly charges"
)

customerentity.createfeature(

    featureid="totalcharges",

    valuetype="DOUBLE",
    description="Total charges to date"
)

customerentity.createfeature(
    featureid="contracttype",
    valuetype="STRING",

    description="Type of contract"
)

# Create multiple features at once
featuresconfig = {
    "age": {"valuetype": "INT64", "description": "Customer age"},

    "tenuremonths": {"valuetype": "INT64", "description": "Tenure in months"},

    "monthlycharges": {"valuetype": "DOUBLE", "description": "Monthly charges"},

    "totalcharges": {"valuetype": "DOUBLE", "description": "Total charges"},

    "contracttype": {"valuetype": "STRING", "description": "Contract type"}

}

customerentity.batchcreatefeatures(featuresconfig)

# Ingest features from BigQuery
customerentity.ingestfrombq(
    featureids=["age", "tenuremonths", "monthlycharges", "totalcharges"],
    featuretime="updatetime",
    bqsourceuri="bq://project.dataset.customerfeatures",

    entityidfield="customerid"
)

2. Ingest from DataFrame

import pandas as pd
from datetime import datetime

Create feature dataframe
df = pd.DataFrame({
    "customerid": ["C001", "C002", "C003"],

    "age": [25, 35, 45],
    "tenuremonths": [12, 24, 36],