Feast feature store basics in MLOps - Mini Project: Build & Apply

Feast feature store basics

📖 Scenario: You are working on a machine learning project that needs to manage and serve features efficiently. You will use Feast, a feature store, to organize and retrieve feature data for your models.

🎯 Goal: Build a simple Feast feature store setup by defining an entity, creating a feature view with features, and retrieving feature data for a sample entity.

📋 What You'll Learn

Define an entity called driver with an ID field

Create a feature view named driver_stats with features conv_rate and acc_rate

Retrieve feature data for a driver with ID 1001

Print the retrieved feature data

💡 Why This Matters

🌍 Real World

Feature stores like Feast help teams manage and serve machine learning features consistently and efficiently, avoiding duplicated work and ensuring fresh data.

💼 Career

Understanding Feast basics is valuable for MLOps engineers and data scientists who build scalable ML pipelines and need reliable feature management.

1

Define the entity for drivers

Create an entity called driver with an ID field named driver_id of type ValueType.INT64.

MLOps

from feast import Entity
from feast.types import ValueType

# Define the driver entity
# Your code here

Need a hint?

Use Entity with name="driver" and value_type=ValueType.INT64.

2

Create a feature view for driver statistics

Create a feature view called driver_stats with features conv_rate and acc_rate both of type Float32. Use the driver entity and set the ttl to 86400 seconds (1 day).

MLOps

from feast import Entity
from feast.types import ValueType

# Define the driver entity
driver = Entity(name="driver", value_type=ValueType.INT64, description="driver id")

from feast import Feature, FeatureView
from feast.types import Float32

# Create the driver_stats feature view
# Your code here

Need a hint?

Use FeatureView with entities=["driver"], two Feature objects, and ttl=86400.

3

Retrieve feature data for a driver

Use the Feast client to get online features for the entity driver with driver_id 1001. Request features conv_rate and acc_rate from the driver_stats feature view.

MLOps

from feast import Entity
from feast.types import ValueType

# Define the driver entity
driver = Entity(name="driver", value_type=ValueType.INT64, description="driver id")

from feast import Feature, FeatureView
from feast.types import Float32

# Create the driver_stats feature view
driver_stats = FeatureView(
    name="driver_stats",
    entities=["driver"],
    features=[
        Feature(name="conv_rate", dtype=Float32),
        Feature(name="acc_rate", dtype=Float32),
    ],
    ttl=86400,
)

from feast import FeatureStore

client = FeatureStore(repo_path=".")

# Retrieve online features for driver_id 1001
# Your code here

Need a hint?

Use client.get_online_features with features list and entity_rows dictionary for driver_id 1001.

4

Print the retrieved feature data

Print the feature_vector dictionary to display the retrieved feature values for conv_rate and acc_rate.

MLOps

Need a hint?

Use print(feature_vector) to display the features.