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

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

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

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

Hint

Use print(feature_vector) to display the features.

Practice

1. What is the main purpose of Feast in machine learning workflows?

easy

A. To store and serve ML features consistently for training and serving

B. To train machine learning models automatically

C. To visualize data trends over time

D. To deploy ML models to production servers

Solution

1. Step 1: Understand Feast's role

   Feast is designed to store and serve features, not to train or deploy models.

2. Step 2: Identify the correct purpose

   It ensures features used in training and serving are consistent and reusable.

3. Final Answer:

   To store and serve ML features consistently for training and serving -> Option A

4. Quick Check:

   Feast = feature store for consistent features [OK]

Hint: Remember Feast is about features, not models or visualization [OK]

Common Mistakes:

• Confusing Feast with model training tools
• Thinking Feast deploys models
• Assuming Feast is for data visualization

2. Which Feast command is used to fetch features for a given entity ID?

easy

A. feast apply

B. feast online-get

C. feast deploy

D. feast materialize

Solution

1. Step 1: Review Feast commands

   feast apply sets up feature definitions, materialize loads data, deploy is not a Feast command.

2. Step 2: Identify fetch command

   feast online-get is used to fetch features for specific entity IDs.

3. Final Answer:

   feast online-get -> Option B

4. Quick Check:

   Fetch features = online-get [OK]

Hint: Fetch features? Use online-get command [OK]

Common Mistakes:

• Using feast apply to fetch features
• Confusing materialize with fetching
• Assuming deploy is a Feast command

3. Given this Python snippet using Feast client:

features = client.get_online_features(
    feature_refs=["driver:conv_rate", "driver:acc_rate"],
    entity_rows=[{"driver_id": 1001}]
).to_dict()
print(features)

What will be the output type of features?

medium

A. A dictionary with feature names as keys and lists of values

B. A list of feature names only

C. A string representation of features

D. An integer count of features fetched

Solution

1. Step 1: Understand get_online_features output

   The method returns an object that can be converted to a dictionary with to_dict().

2. Step 2: Analyze the dictionary structure

   The dictionary keys are feature names, and values are lists of feature values for each entity row.

3. Final Answer:

   A dictionary with feature names as keys and lists of values -> Option A

4. Quick Check:

   to_dict() output = dict of feature lists [OK]

Hint: to_dict() returns dict with feature keys and value lists [OK]

Common Mistakes:

• Expecting a list instead of dict
• Thinking output is a string
• Assuming output is a count number

4. You run feast online-get but get an error: Entity ID not found. What is the most likely cause?

medium

A. The Feast CLI is not installed

B. The feature references are misspelled

C. The feature store is offline

D. The entity ID used does not exist in the feature store

Solution

1. Step 1: Understand the error message

   'Entity ID not found' means the requested entity ID is missing in the store.

2. Step 2: Check other options

   CLI not installed or store offline would cause different errors; misspelled features cause feature errors, not entity ID errors.

3. Final Answer:

   The entity ID used does not exist in the feature store -> Option D

4. Quick Check:

   Entity ID error = missing entity ID [OK]

Hint: Entity ID error means ID missing in store, not CLI or spelling [OK]

Common Mistakes:

• Assuming CLI is missing
• Blaming feature names for entity ID errors
• Thinking store is offline without checking

5. You want to keep training and serving data consistent using Feast. Which two steps should you perform? Select the best pair.

hard

A. Fetch features randomly during serving, then define features later

B. Train model first, then define features in Feast after training

C. Define features in Feast, then fetch features by entity IDs during serving

D. Store raw data only, and transform features outside Feast

Solution

1. Step 1: Understand Feast's role in consistency

   Feast ensures features are defined once and reused for training and serving.

2. Step 2: Identify correct workflow

   Defining features first and fetching by entity IDs during serving keeps data consistent.

3. Final Answer:

   Define features in Feast, then fetch features by entity IDs during serving -> Option C

4. Quick Check:

   Define then fetch = consistent features [OK]

Hint: Define features first, fetch by entity IDs for consistency [OK]

Common Mistakes:

• Training before defining features
• Fetching features randomly
• Ignoring Feast for feature transformations