Feast feature store basics in MLOps - Step-by-Step Execution

Process Flow - Feast feature store basics

Define Features

↓

Ingest Data

↓

Store Features in Feast

↓

Serve Features

↓

Use Features for ML Training or Prediction

This flow shows how you define, ingest, store, and serve features using Feast for machine learning.

Execution Sample

MLOps

from feast import FeatureStore
store = FeatureStore(repo_path="./feature_repo")
features = store.get_historical_features(entity_df=entity_df, features=["driver_stats:conv_rate"])
print(features.head())

This code connects to Feast, fetches historical features for an entity, and prints the first rows.

Process Table

Step	Action	Input	Output	Notes
1	Initialize FeatureStore	repo_path='./feature_repo'	FeatureStore object created	Connects to local Feast repo
2	Prepare entity dataframe	entity_df with driver IDs and timestamps	entity_df ready	Defines which entities to fetch features for
3	Call get_historical_features	entity_df, features=['driver_stats:conv_rate']	DataFrame with features	Fetches feature data for entities
4	Print features head	features DataFrame	Prints first 5 rows	Shows feature values for entities
5	End	N/A	N/A	Feature retrieval complete

💡 All steps completed successfully; features fetched and displayed

Status Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4	Final
store	None	FeatureStore object	FeatureStore object	FeatureStore object	FeatureStore object	FeatureStore object
entity_df	None	None	DataFrame with entities	DataFrame with entities	DataFrame with entities	DataFrame with entities
features	None	None	None	DataFrame with features	DataFrame with features	DataFrame with features

Key Moments - 2 Insights

Why do we need to prepare an entity dataframe before fetching features?

The entity dataframe tells Feast which specific entities (like drivers) and timestamps to get features for, as shown in step 2 and 3 of the execution_table.

What does get_historical_features return?

It returns a DataFrame containing feature values for the requested entities and times, as seen in step 3 and 4 where features are fetched and printed.

Visual Quiz - 3 Questions

Look at the execution_table, what is the output after step 3?

A FeatureStore object

B DataFrame with features

C entity_df with driver IDs

D None

💡 Hint

Check the 'Output' column for step 3 in the execution_table.

At which step is the entity dataframe prepared?

A Step 1

B Step 3

C Step 2

D Step 4

💡 Hint

Look at the 'Action' column in the execution_table for when entity_df is ready.

If the entity dataframe is empty, what would happen at step 3?

A An empty DataFrame would be returned

B Features would be fetched normally

C An error would occur

D The FeatureStore object would be None

💡 Hint

Think about what happens when no entities are provided to get_historical_features.

Concept Snapshot

Feast Feature Store Basics:
- Define features in a repo
- Prepare entity dataframe with IDs and timestamps
- Use FeatureStore.get_historical_features to fetch data
- Returns DataFrame of features
- Used for ML training and prediction

Full Transcript

Feast is a tool to manage features for machine learning. First, you define your features in a repository. Then, you prepare an entity dataframe that lists the entities and times you want features for. Using the FeatureStore object, you call get_historical_features with this dataframe and the feature names. Feast returns a DataFrame with the feature values for those entities and times. This data can be used to train or serve ML models. The process involves initializing the store, preparing entities, fetching features, and using the results.

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