Скачать или смотреть 🔥 Real-Time Feature Store with MySQL + Redis + Kafka — The Future of ML Automation & AI Pipelines!

Welcome to one of the most powerful concepts in Machine Learning engineering:
🔥 Real-Time Feature Stores
— the backbone of recommendation engines, fraud detection systems, personalization platforms, and LLM-driven applications.

Companies like Netflix, Uber, Amazon, DoorDash, Meta all use Feature Stores to guarantee that ML models receive accurate, fresh, and consistent features in milliseconds.

Let’s break down each interview question with real examples 👇

1) What Is a Real-Time Feature Store & Why It Matters?

A Feature Store centralizes how ML features are:

engineered

stored

served

kept up-to-date

shared across teams

It contains:

Offline Features → stored in MySQL/Postgres/Data Warehouse

Online Features → cached in Redis for ultra-fast inference

This solves the biggest ML pain point: training-serving skew.
Models finally get the same features during training and inference.

2) Build a Low-Latency Feature Store using MySQL + Redis + Kafka
🔹 Offline Store (MySQL) → batch features
SELECT customer_id,
SUM(amount) AS total_spent_last_30_days,
COUNT(order_id) AS purchase_count
FROM orders
WHERE order_date v= NOW() - INTERVAL 30 DAY
GROUP BY customer_id;

🔹 Kafka Streams → real-time updates

Every new order:
MySQL → Debezium CDC → Kafka → Feature Updater

🔹 Redis → online low-latency features
redis.hset(f"user:{uid}", mapping={
"recent_avg": avg_purchase,
"last_amount": last_amount
})


This creates an EVENT-DRIVEN FEATURE PIPELINE.

3) Online + Offline Feature Sync

You combine the long-term stats from MySQL with fresh events from Redis.

Example at inference time:

offline = mysql_query("SELECT total_spent_last_30_days FROM agg_features WHERE id=%s", uid)
online = redis.hgetall(f"user:{uid}")

features = {**offline, **online}


This is how Amazon always knows:
➡️ "What you recently viewed" + "Your historical preferences".

4) Serving ML Predictions with Feature Store + FastAPI

Your prediction server no longer queries raw databases.
It pulls processed features → runs model → returns result instantly.

Example FastAPI endpoint:
@app.get("/predict")
def predict(uid: int):
features = get_feature_vector(uid) # MySQL + Redis merged
pred = model.predict([features])[0]
return {"user": uid, "prediction": int(pred)}


Inference time: single-digit milliseconds.
This is why industrial ML systems scale.

5) Ensuring Feature Consistency, Freshness & Correctness

To keep features reliable:

🔹 Consistency

Same transformation logic for training + inference

Store transformations in reusable Python modules

🔹 Freshness

Kafka updates online features within seconds

Redis TTL to auto-expire stale values

🔹 Correctness

Unit tests for feature pipelines

Schema checks on MySQL tables

Drift detection on feature distributions

This is pure MLOps excellence — and a common interview topic in 2025.