Feature Stores

Feature Stores Skill

Learn: Build production feature stores for ML systems.

Skill Overview

Learning Objectives

1. Understand feature store architecture
2. Implement features with Feast
3. Validate data quality with Great Expectations
4. Serve features online and offline
5. Version datasets with DVC

Topics Covered

Module 1: Feature Store Architecture (8 hours)

Components:

┌─────────────────────────────────────────────────────────────┐
│                   FEATURE STORE ARCHITECTURE                 │
├─────────────────────────────────────────────────────────────┤
│                                                              │
│  ┌─────────────┐    ┌─────────────┐    ┌─────────────┐     │
│  │ Offline     │    │  Feature    │    │  Online     │     │
│  │ Store       │───▶│  Registry   │◀───│  Store      │     │
│  │ (Parquet)   │    │  (Metadata) │    │  (Redis)    │     │
│  └─────────────┘    └─────────────┘    └─────────────┘     │
│        │                   │                   │            │
│        ▼                   ▼                   ▼            │
│   [Training]         [Discovery]        [Inference]        │
│                                                              │
└─────────────────────────────────────────────────────────────┘

Exercises:

• Design feature store for e-commerce use case
• Compare Feast vs Tecton vs Hopsworks

Module 2: Feast Implementation (12 hours)

Feature Definition Example:

from feast import Entity, Feature, FeatureView, FileSource
from feast.types import Float32, Int64
from datetime import timedelta

# Entity definition
customer = Entity(
    name="customer_id",
    value_type=ValueType.INT64,
    description="Customer identifier"
)

# Feature view
customer_features = FeatureView(
    name="customer_features",
    entities=["customer_id"],
    ttl=timedelta(days=7),
    schema=[
        Feature(name="total_purchases", dtype=Float32),
        Feature(name="avg_order_value", dtype=Float32),
        Feature(name="days_since_last_order", dtype=Int64),
    ],
   
    source=customer_stats_source
)

Exercises:

• Set up Feast repository locally
• Create entity and feature views
• Materialize features to online store
• Retrieve features for training and inference

Module 3: Data Validation (8 hours)

Great Expectations Setup:

import great_expectations as gx

# Create validation suite
suite = context.add_expectation_suite("ml_data_validation")

# Add expectations
suite.add_expectation(
    gx.expectations.ExpectColumnValuesToNotBeNull(
        column="target",
        mostly=0.99
    )
)

suite.add_expectation(
    gx.expectations.ExpectColumnMeanToBeBetween(
        column="feature_a",
        min_value=0.0,
        max_value=100.0
    )
)

Module 4: Data Versioning (7 hours)

DVC Workflow:

# Initialize DVC
dvc init

# Add data to tracking
dvc add data/training_data.parquet

# Push to remote storage
dvc push

# Checkout specific version
git checkout v1.0.0
dvc checkout

Code Templates

Template: Feature Engineering Pipeline

# templates/feature_pipeline.py
from sklearn.base import BaseEstimator, TransformerMixin
import pandas as pd

class FeaturePipeline(BaseEstimator, TransformerMixin):
    """Production feature engineering pipeline."""

    def __init__(self, config: dict):
        self.config = config
        self.feature_names = []

    def fit(self, X: pd.DataFrame, y=None):
        """Learn feature statistics."""
        self.means = X.select_dtypes(include=['number']).mean()
        self.stds = X.select_dtypes(include=['number']).std()
        return self

    def transform(self, X: pd.DataFrame) -> pd.DataFrame:
        """Apply feature transformations."""
        X = X.copy()

        # Numerical normalization
        for col in X.select_dtypes(include=['number']).columns:
            X[f"{col}_normalized"] = (X[col] - self.means[col]) / self.stds[col]

        # Temporal features
        for col in self.config.get("datetime_columns", []):
            X[f"{col}_hour"] = pd.to_datetime(X[col]).dt.hour
            X[f"{col}_dow"] = pd.to_datetime(X[col]).dt.dayofweek

        return X

Troubleshooting Guide

Resources

• Feast Documentation
• Great Expectations Docs
• DVC Documentation
• [See: training-pipelines] - Use features in training

Version History