A hands-on crash course on Feature Stores using Feast with a fraud detection scenario.
This repository contains a single Jupyter notebook that teaches Feature Stores by showing you the problems first, then solving them systematically. No hand-waving, no toy examples—just real production challenges and their solutions.
Through a fraud detection use case, you'll understand:
- Training-Serving Skew - Why your Pandas features break in production SQL
- Feature Duplication - How teams waste time reimplementing the same features
- Latency Issues - Why on-demand feature computation kills your SLA
- Data Leakage - How point-in-time correctness prevents future peeking
- Feature Governance - Why scattered features create organizational chaos
Then you'll see how Feast solves all five problems with:
- Centralized feature registry
- Single source of truth (no more Pandas vs SQL)
- Pre-materialized online store (5ms instead of 45ms)
- Built-in point-in-time joins (data leakage impossible by design)
- Feature discovery and versioning
Clone or download the .ipynb file.
The notebook is organized in a problem → solution flow:
- Setup (Cells 1-3): Install Feast and initialize repository
- Problem #1: Training-Serving Skew (Cells 4-9): See how dual implementations diverge
- Problem #2: Feature Duplication (Cells 10-11): Watch teams reimplement the same logic
- Problem #3: Latency (Cells 12-14): Measure the cost of on-demand computation
- Problem #4: Data Leakage (Cells 15-18): Compare models with/without point-in-time joins
- Problem #5: Governance (Cells 19-20): Quantify the discovery chaos
- Solution Part 1 (Cells 21-23): Define features as code with Feast
- Solution Part 2 (Cells 24-25): Get automatic point-in-time correctness
- Solution Part 3 (Cells 26-27): Achieve 8x faster serving with materialization
- Final Comparison (Cell 28): See before/after metrics
Total runtime: ~10 minutes
| Metric | Without Feature Store | With Feast |
|---|---|---|
| Latency (P95) | 48ms | 6.5ms |
| Point-in-Time Correctness | Manual (error-prone) | Automatic |
| Code Duplication | DS (Pandas) + Eng (SQL) | Single definition |
| Feature Discovery | 4.3 hours average | Instant (centralized registry) |
✅ Use when:
- You have 3+ models in production
- You need latency < 50ms
- Multiple teams share features
- You've been burned by training-serving skew
❌ Skip when:
- Running 1-2 models only
- Doing batch scoring (latency doesn't matter)
- Pure research mode (too early to standardize)
- Team < 3 people (overhead exceeds benefits)
- Basic Python and Pandas knowledge
- Understanding of ML training/serving workflows
- Familiarity with the pain of production ML (optional but helpful)
- Feature Store: Feast 0.40+
- ML Framework: scikit-learn
- Data: Pandas, NumPy
- Visualization: Matplotlib, Seaborn
- Storage: SQLite (local demo), easily swappable for Redis/DynamoDB in production
For a detailed walkthrough with explanations of each problem and solution, check out the companion blog post: [link to your blog post] or https://igorcomune.medium.com/mlops-a-free-crash-course-about-feature-store-with-google-colab-b447d87dd075
MIT License
- Built with Feast
- Inspired by real production ML pain points
- Fraud detection scenario is synthetic but represents real patterns
