feature-stores

name	feature-stores
version	2.0.0
sasmp_version	1.3.0
description	Master feature stores - Feast, data validation, versioning, online/offline serving
bonded_agent	03-data-pipelines
bond_type	PRIMARY_BOND
category	data_engineering
difficulty	intermediate_to_advanced
estimated_hours	35
prerequisites	["mlops-basics"]
validation	{"pre_conditions":["Completed mlops-basics skill","Understanding of data pipelines"],"post_conditions":["Can design feature store architecture","Can implement features with Feast","Can validate data quality"]}
observability	{"metrics":["features_created","validation_checks_passed","latency_measurements"]}

Feature Stores Skill

Learn: Build production feature stores for ML systems.

Skill Overview

Attribute	Value
Bonded Agent	03-data-pipelines
Difficulty	Intermediate to Advanced
Duration	35 hours
Prerequisites	mlops-basics

---

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  Float32, Int64
 datetime  timedelta


customer = Entity(
    name=,
    value_type=ValueType.INT64,
    description=
)


customer_features = FeatureView(
    name=,
    entities=[],
    ttl=timedelta(days=),
    schema=[
        Feature(name=, dtype=Float32),
        Feature(name=, dtype=Float32),
        Feature(name=, dtype=Int64),
    ],
    online=,
    source=customer_stats_source
)