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

Process Flow - Feast feature store basics

Define Features

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Ingest Data

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Store Features in Feast

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Serve Features

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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.